Process parameter estimation in controlling emission of a non-particulate pollutant into the air

ABSTRACT

A parameter value estimator is provided for a process performed primarily to control emission of a particular non-particulate pollutant, such as NO x  and SO 2 , into the air. The process has multiple process parameters (MPPs) including a parameter representing an amount of the particular non-particulate pollutant emitted. The parameter value estimator includes either a neural network process model or a non-neural network process model. In either case the model represents a relationship between one of the MPPs, other than the parameter representing the amount of the emitted particular non-particulate pollutant, and one or more other of the MPPs. Also included is a processor configured with the logic, e.g. programmed software, to estimate a value of the one MPP based on a value of each of the one or more other MPPs and the one model.

RELATED APPLICATIONS

This application is a Continuation application of U.S. application Ser.No. 10/927,243, now U.S. Pat. No. 7,536,232, filed Aug. 27, 2004,entitled MODEL PREDICTIVE CONTROL OF AIR POLLUTION CONTROL PROCESSES;which is related to U.S. application Ser. No. 10/927,229 now U.S. Pat.No. 7,522,963, filed Aug. 27, 2004, entitled OPTIMIZED AIR POLLUTIONCONTROL; U.S. application Ser. No. 10/927,049, filed Aug. 27, 2004,entitled COST BASED CONTROL OF AIR POLLUTION CONTROL; U.S. applicationSer. No. 11/002,436, filed Dec. 3, 2004, entitled APC PROCESS PARAMETERESTIMATION; U.S. application Ser. No. 11/002,439, filed Dec. 3, 2004,entitled APC PROCESS CONTROL WHEN PROCESS PARAMETERS ARE INACCURATELYMEASURED; U.S. application Ser. No. 10/927,201 now U.S. Pat. No.7,113,835, filed Aug. 27, 2004, entitled CONTROL OF ROLLING OR MOVINGAVERAGE VALUES OF AIR POLLUTION CONTROL EMISSIONS TO A DESIRED VALUE;U.S. application Ser. No. 10/926,991 now U.S. Pat. No. 7,117,046, filedAug. 27, 2004, entitled CASCADED CONTROL OF AN AVERAGE VALUE OF APROCESS PARAMETER TO A DESIRED VALUE; U.S. application Ser. No.10/927,200, filed Aug, 27, 2004, entitled MAXIMIZING PROFIT ANDMINIMIZING LOSSES IN CONTROLLING AIR POLLUTION; U.S. application Ser.No. 10/927,221 now U.S. Pat. No. 7,323,036, filed Aug. 27, 2004,entitled MAXIMIZING REGULATORY CREDITS IN CONTROLLING AIR POLLUTION, thecontents of which are incorporated by reference herein in theirentirety.

BACKGROUND OF THE INVENTION

1. Field of Endeavor

The present invention relates generally to process control. Moreparticularly the present invention relates to techniques for enhancedcontrol of processes, such as those utilized for air pollution control.Examples of such processes include but are not limited to wet and dryflue gas desulfurization (WFGD/DFGD), nitrogen oxide removal viaselective catalytic reduction (SCR), and particulate removal viaelectrostatic precipitation (ESP).

2. Background

Wet Flue Gas Desulfurization:

As noted, there are several air pollution control processes, to form abasis for discussion; the WFGD process will be highlighted. The WFGDprocess is the most commonly used process for removal of SO₂ from fluegas in the power industry. FIG. 1, is a block diagram depicting anoverview of a wet flue gas desulfurization (WFGD) subsystem for removingSO₂ from the dirty flue gas, such as that produced by fossil fuel, e.g.coal, fired power generation systems, and producing a commercial gradebyproduct, such as one having attributes which will allow it to bedisposed of at a minimized disposal cost, or one having attributesmaking it saleable for commercial use.

In the United States of America, the presently preferred byproduct ofWFGD is commercial grade gypsum having a relatively high quality (95+%pure) suitable for use in wallboard, which is in turn used in home andoffice construction. Commercial grade gypsum of high quality (˜92%) isalso the presently preferred byproduct of WFGD in the European Union andAsia, but is more typically produced for use in cement, and fertilizer.However, should there be a decline in the market for higher qualitygypsum, the quality of the commercial grade gypsum produced as abyproduct of WFGD could be reduced to meet the less demanding qualityspecifications required for disposal of at minimum costs. In thisregard, the cost of disposal may be minimized if, for example, thegypsum quality is suitable for either residential landfill or forbackfilling areas from which the coal utilized in generating power hasbeen harvested.

As shown in FIG. 1, dirty, SO₂ laden flue gas 112 is exhausted from aboiler or economizer (not shown) of a coal fired power generation system110 to the air pollution control system (APC) 120. Commonly the dirtyflue gas 112 entering the APC 120 is not only laden with SO₂, but alsocontains other so called pollutants such as NO_(x) and particulatematter. Before being processed by the WFGD subsystem, the dirty flue gas112 entering the APC 120 is first directed to other APC subsystems 122in order remove NO_(x) and particulate matter from the dirty flue gas112. For example, the dirty flue gas may be processed via a selectivecatalytic reduction (SCR) subsystem (not shown) to remove NO_(x) and viaan electrostatic precipitator subsystem (EPS) (not shown) or filter (notshown) to remove particulate matter.

The SO₂ laden flue gas 114 exhausted from the other APC subsystems 122is directed to the WFGD subsystem 130. SO₂ laden flue gas 114 isprocessed by the absorber tower 132. As will be understood by thoseskilled in the art, the SO₂ in the flue gas 114 has a high acidconcentration. Accordingly, the absorber tower 132 operates to place theSO₂ laden flue gas 114 in contact with liquid slurry 148 having a higherpH level than that of the flue gas 114.

It will be recognized that most conventional WFGD subsystems include aWFGD processing unit of the type shown in FIG. 1. This is true, for manyreasons. For example, as is well understood in the art, WFGD processingunits having a spray absorber towers have certain desirable processcharacteristics for the WFGD process. However, WFGD processing unitshaving other absorption/oxidation equipment configurations could, ifdesired, be utilized in lieu of that shown in FIG. 1 and still providesimilar flue gas desulfurization functionality and achieve similarbenefits from the advanced process control improvements presented inthis application. For purposes of clarity and brevity, this discussionwill reference the common spray tower depicted in FIG. 1, but it shouldbe noted that the concepts presented could be applied to other WFGDconfigurations.

During processing in the countercurrent absorber tower 132, the SO₂ inthe flue gas 114 will react with the calcium carbonate-rich slurry(limestone and water) 148 to form calcium sulfite, which is basically asalt and thereby removing the SO₂ from the flue gas 114. The SO₂ cleanedflue gas 116 is exhausted from the absorber tower 132, either to anexhaust stack 117 or to down-steam processing equipment (not shown). Theresulting transformed slurry 144 is directed to the crystallizer 134,where the salt is crystallized. The crystallizer 134 and the absorber132 typically reside in a single tower with no physical separationbetween them—while there are different functions (absorption in the gasphase and crystallization in the liquid phase) going on, the twofunctions occur in the same process vessel. From here, gypsum slurry146, which includes the crystallized salt, is directed from thecrystallizer 134 to the dewatering unit 136. Additionally, recycleslurry 148, which may or may not include the same concentration ofcrystallized salts as the gypsum slurry 146, is directed from thecrystallizer 134 through pumps 133 and back to the absorber tower 132 tocontinue absorption cycle.

The blower 150 pressurizes ambient air 152 to create oxidation air 154for the crystallizer 134. The oxidation air 154 is mixed with the slurryin the crystallizer 134 to oxidize the calcium sulfite to calciumsulfate. Each molecule of calcium sulfate binds with two molecules ofwater to form a compound that is commonly referred to as gypsum 160. Asshown, the gypsum 160 is removed from the WFGD processing unit 130 andsold to, for example manufacturers of construction grade wallboard.

Recovered water 167, from the dewatering unit 136 is directed to themixer/pump 140 where it is combined with fresh ground limestone 174 fromthe grinder 170 to create limestone slurry. Since some process water islost to both the gypsum 160 and the waste stream 169, additional freshwater 162, from a fresh water source 164, is added to maintain thelimestone slurry density. Additionally, waste, such as ash, is removedfrom the WFGD processing unit 130 via waste stream 169. The waste could,for example, be directed to an ash pond or disposed of in anothermanner.

In summary, the SO₂ within the SO₂ laden flue gas 114 is absorbed by theslurry 148 in the slurry contacting area of the absorber tower 132, andthen crystallized and oxidized in the crystallizer 134 and dewatered inthe dewatering unit 136 to form the desired process byproduct, which inthis example, is commercial grade gypsum 160. The SO₂ laden flue gas 114passes through the absorber tower 132 in a matter of seconds. Thecomplete crystallization of the salt within the transformed slurry 144by the crystallizer 134 may require from 8 hours to 20+ hours. Hence,the crystallizer 134 has a large volume that serves as a slurryreservoir crystallization. The recycle slurry 148 is pumped back to thetop of the absorber to recover additional SO₂.

As shown, the slurry 148 is fed to an upper portion of the absorbertower 132. The tower 132 typically incorporates multiple levels of spraynozzles to feed the slurry 148 into the tower 132. The absorber 132, isoperated in a countercurrent configuration: the slurry spray flowsdownward in the absorber and comes into contact with the upward flowingSO₂ laden flue gas 114 which has been fed to a lower portion of theabsorber tower.

Fresh limestone 172, from limestone source 176, is first ground in thegrinder 170 (typically a ball mill) and then mixed with (recovered water167 and fresh/make-up water 162 in a mixer 140 to form limestone slurry141. The flow of the ground limestone 174 and water 162 via valve (notshown) to the mixer/tank 140 are controlled to maintain a sufficientinventory of fresh limestone slurry 141 in the mixer/tank 140. The flowof fresh limestone slurry 141 to the crystallizer 134 is adjusted tomaintain an appropriate pH for the slurry 148, which in turn controlsthe amount of SO₂ removed from the flue gas 114. WFGD processingtypically accomplishes 92-97% removal of SO₂ from the flue gas, althoughthose skilled in the art will recognize that but utilizing certaintechniques and adding organic acids to the slurry the removal of SO₂ canincrease to greater than 97%.

As discussed above, conventional WFGD subsystems recycle the slurry.Although some waste water and other waste will typically be generated inthe production of the gypsum, water is reclaimed to the extent possibleand used to make up fresh limestone slurry, thereby minimizing waste andcosts, which would be incurred to treat the process water.

It will be recognized that because limestone is readily available inlarge quantities in most locations, it is commonly used as the reactantin coal gas desulfurization processing. However, other reactants, suchas quick lime or a sodium compound, could alternatively be used, in lieuof limestone. These other reactants are typically more expensive and arenot currently cost-competitive with the limestone reactant. However,with very slight modifications to the mixer 140 and upstream reactantsource, an existing limestone WFGD could be operated using quick lime ora sodium compound. In fact, most WFGD systems include a lime backupsubsystem so the WFGD can be operated if there are problems withlimestone delivery and/or extended maintenance issues with the grinder170.

FIG. 2 further details certain aspects of the WFGD subsystem shown inFIG. 1. As shown, the dewatering unit 136 may include both a primarydewatering unit 136A and a secondary dewatering unit 136B. The primarydewatering unit 136A preferably includes hydrocyclones for separatingthe gypsum and water. The secondary dewatering unit 136B preferablyincludes a belt dryer for drying the gypsum. As has been previouslydiscussed, the flue gas 114 enters the absorber 132, typically from theside, and flows upward through a limestone slurry mist that is sprayedinto the upper portion of the absorber tower. Prior to exiting theabsorber, the flue gas is put through a mist eliminator (ME) (not shown)that is located in the top of the absorber 132; the mist eliminatorremoves entrained liquid and solids from the flue gas stream. To keepthe mist eliminator clean of solids, a ME water wash 200 applied to themist eliminator. As will be understood, the ME wash 200 keeps the MEclean within the absorber tower 132 with water from the fresh watersource 164. The ME wash water 200 is the purest water fed to the WFGDsubsystem 130.

As noted above, the limestone slurry mist absorbs a large percentage ofthe SO₂ (e.g., 92-97%) from the flue gas that is flowing through theabsorber tower 132. After absorbing the SO₂, the slurry spray drops tothe crystallizer 134. In a practical implementation, the absorber tower132 and the crystallizer 134 are often housed in a single unitarystructure, with the absorber tower located directly above thecrystallizer within the structure. In such implementations, the slurryspray simply drops to the bottom of the unitary structure to becrystallized.

The limestone slurry reacts with the SO₂ to produce gypsum (calciumsulfate dehydrate) in the crystallizer 134. As previously noted, forced,compressed oxidation air 154 is used to aid in oxidation, which occursin the following reaction:SO₂+CaCO₃+1/2O₂+2H₂O→CaSO₄.2H₂O+CO₂   (1)The oxidation air 154 is forced into the crystallizer 134, by blower150. Oxidation air provides additional oxygen needed for the conversionof the calcium sulfite to calcium sulfate.

The absorber tower 132 is used to accomplish the intimate fluegas/liquid slurry contact necessary to achieve the high removalefficiencies required by environmental specifications. Countercurrentopen-spray absorber towers provide particularly desirablecharacteristics for limestone-gypsum WFGD processing: they areinherently reliable, have lower plugging potential than othertower-based WFGD processing unit components, induce low pressure drop,and are cost-effective from both a capital and an operating costperspective.

As shown in FIG. 2, the water source 164 typically includes a water tank164A for storing a sufficient quantity of fresh water. Also typicallyincluded there is one or more pumps 164B for pressurizing the ME wash200 to the absorber tower 132, and one or more pumps 164C forpressurizing the fresh water flow 162 to the mixer 140. The mixer 140includes a mixing tank 140A and one more slurry pumps 140B to move thefresh limestone slurry 141 to the crystallizer 134. One or moreadditional very large slurry pumps 133 (see FIG. 1) are required to liftthe slurry 148 from the crystallizer 134 to the multiple spray levels inthe top of the absorber tower 132.

As will be described further below, typically, the limestone slurry 148enters the absorber tower 132, via spray nozzles (not shown) disposed atvarious levels of the absorber tower 132. When at full load, most WFGDsubsystems operate with at least one spare slurry pump 133. At reducedloads, it is often possible to achieve the required SO₂ removalefficiency with a reduced number of slurry pumps 133. There issignificant economic incentive to reduce the pumping load of the slurrypumps 133. These pumps are some of the largest pumps in the world andthey are driven by electricity that could otherwise be sold directly tothe power grid (parasitic power load).

The gypsum 160 is separated from liquids in the gypsum slurry 146 in theprimary dewaterer unit 136A, typically using a hydrocyclone. Theoverflow of the hydrocyclone, and/or one or more other components ofprimary dewaterer unit 136A, contains a small amount of solids. As shownin FIG. 2, this overflow slurry 146A is returned to the crystallizer134. The recovered water 167 is sent back to mixer 140 to make freshlimestone slurry. The other waste 168 is commonly directed from theprimary dewaterer unit 136A to an ash pond 210. The underflow slurry 202is directed to the secondary dewaterer unit 136B, which often takes theform of a belt filter, where it is dried to produce the gypsum byproduct160. Again, recovered water 167 from the secondary dewaterer unit 136Bis returned to the mixer/pump 140. As shown in FIG. 1, hand or othergypsum samples 161 are taken and analyzed, typically every few hours, todetermine the purity of the gypsum 160. No direct on-line measurement ofgypsum purity is conventionally available.

As shown in FIG. 1, a proportional integral derivative (PID) controller180 is conventionally utilized in conjunction with a feedforwardcontroller (FF) 190 to control the operation of the WFDG subsystem.Historically, PID controllers directed pneumatic analog controlfunctions. Today, PID controllers direct digital control functions,using mathematically formulations. The goal of FF 190/PID controller 180is to control the slurry pH, based on an established linkage. Forexample, there could be an established linkage between the adjustment ofvalve 199 shown in FIG. 1, and a measured pH value of slurry 148 flowingfrom the crystallizer 134 to the absorber tower 132. If so, valve 199 iscontrolled so that the pH of the slurry 148 corresponds to a desiredvalue 186, often referred to as a setpoint (SP).

The FF 190/PID controller 180 will adjust the flow of the limestoneslurry 141 through valve 199, based on the pH setpoint, to increase ordecrease the pH value of the slurry 148 measured by the pH sensor 182.As will be understood, this is accomplish by the FF/PID controllertransmitting respective control signals 181 and 191, which result in avalve adjustment instruction, shown as flow control SP 196, to a flowcontroller which preferably is part of the valve 199. Responsive to flowcontrol SP 196, the flow controller in turn directs an adjustment of thevalve 199 to modify the flow of the limestone slurry 141 from themixer/pump 140 to the crystallizer 134.

The present example shows pH control using the combination of the FFcontroller 190 and the PID controller 180. Some installations will notinclude the FF controller 190.

In the present example, the PID controller 180 generates the PID controlsignal 181 by processing the measured slurry pH value 183 received fromthe pH sensor 182, in accordance with a limestone flow control algorithmrepresenting an established linkage between the measured pH value 183 ofthe slurry 148 flowing from the crystallizer 134 to the absorber tower132. The algorithm is typically stored at the PID controller 180,although this is not mandatory. The control signal 181 may represent,for example, a valve setpoint (VSP) for the valve 199 or for a measuredvalue setpoint (MVSP) for the flow of the ground limestone slurry 141exiting the valve 199.

As is well understood in the art, the algorithm used by the PIDcontroller 180 has a proportional element, an integral element, and aderivative element. The PID controller 180 first calculates thedifference between the desired SP and the measured value, to determinean error. The PID controller next applies the error to the proportionalelement of the algorithm, which is an adjustable constant for the PIDcontroller, or for each of the PID controllers if multiple PIDcontrollers are used in the WFGD subsystem. The PID controller typicallymultiples a tuning factor or process gain by the error to obtain aproportional function for adjustment of the valve 199.

However, if the PID controller 180 does not have the correct value forthe tuning factor or process gain, or if the process conditions arechanging, the proportional function will be imprecise. Because of thisimprecision, the VSP or MVSP generated by the PID controller 180 willactually have an offset from that corresponding to the desired SP.Accordingly, the PID controller 180 applies the accumulated error overtime using the integral element. The integral element is a time factor.Here again, the PID controller 180 multiplies a tuning factor or processgain by the accumulated error to eliminate the offset.

Turning now to the derivative element. The derivative element is anacceleration factor, associated with continuing change. In practice, thederivative element is rarely applied in PID controllers used forcontrolling WFGD processes. This is because application of thederivative element is not particularly beneficial for this type ofcontrol application. Thus, most controllers used for in WFGD subsystemsare actually PI controllers. However, those skilled in the art willrecognize that, if desired, the PID controller 180 could be easilyconfigured with the necessary logic to apply a derivative element in aconventional manner.

In summary, there are three tuning constants, which may be applied byconventional PID controllers to control a process value, such as the pHof the recycle slurry 148 entering the absorber tower 132, to asetpoint, such as the flow of fresh lime stone slurry 141 to thecrystallizer 134. Whatever setpoint is utilized, it is alwaysestablished in terms of the process value, not in terms of a desiredresult, such as a value of SO₂ remaining in the flue gas 116 exhaustedfrom the absorber tower 132. Stated another way, the setpoint isidentified in process terms, and it is necessary that the controlledprocess value be directly measurable in order for the PID controller tobe able to control it. While the exact form of the algorithm may changefrom one equipment vendor to another, the basic PID control algorithmhas been in use in the process industries for well over 75 years.

Referring again to FIGS. 1 and 2, based on the received instruction fromthe PID controller 180 and the FF controller 190, the flow controllergenerates a signal, which causes the valve 199 to open or close, therebyincreasing or decreasing the flow of the ground limestone slurry 141.The flow controller continues control of the valve adjustment until thevalve 199 has been opened or closed to match the VSP or the measuredvalue of the amount of limestone slurry 141 flowing to from the valve1992 matches the MVSP.

In the exemplary conventional WFGD control described above, the pH ofthe slurry 148 is controlled based on a desired pH setpoint 186. Toperform the control, the PID 180 receives a process value, i.e. themeasured value of the pH 183 of the slurry 148, from the sensor 182. ThePID controller 180 processes the process value to generate instructions181 to the valve 199 to adjust the flow of fresh limestone slurry 141,which has a higher pH than the crystallizer slurry 144, from themixer/tank 140, and thereby adjust the pH of the slurry 148. If theinstructions 181 result in a further opening of the valve 199, morelimestone slurry 141 will flow from the mixer 140 and into thecrystallizer 134, resulting in an increase in the pH of the slurry 148.On the other hand, if the instructions 181 result in a closing of thevalve 199, less limestone slurry 141 will flow from the mixer 140 andtherefore into the crystallizer 134, resulting in a decrease in the pHof the slurry 148.

Additionally, the WFGD subsystem may incorporate a feed forward loop,which is implemented using a feed forward unit 190 in order to ensurestable operation. As shown in FIG. 1, the concentration value of SO₂ 189in the flue gas 114 entering the absorber tower 132 is measured bysensor 188 and input to the feed forward unit 190. Many WFGD systemsthat include the FF control element may combine the incoming flue gasSO₂ concentration 189 with a measure of generator load from the PowerGeneration System 110, to determine the quantity of inlet SO₂ ratherthan just the concentration and, then use this quantity of inlet SO₂ asthe input to FF 190. The feed forward unit 190 serves as a proportionalelement with a time delay.

In the exemplary implementation under discussion, the feed forward unit190 receives a sequence of SO₂ measurements 189 from the sensor 188. Thefeed forward unit 190 compares the currently received concentrationvalue with the concentration value received immediately preceding thecurrently received value. If the feed forward unit 190 determines that achange in the measured concentrations of SO₂ has occurred, for examplefrom 1000-1200 parts per million, it is configured with the logic tosmooth the step function, thereby avoiding an abrupt change inoperations.

The feed forward loop dramatically improves the stability of normaloperations because the relationship between the pH value of the slurry148 and the amount of limestone slurry 141 flowing to the crystallizer134 is highly nonlinear, and the PID controller 180 is effectively alinear controller. Thus, without the feed forward loop, it is verydifficult for the PID 180 to provide adequate control over a wide rangeof pH with the same tuning constants.

By controlling the pH of the slurry 148, the PID controller 180 effectsboth the removal of SO₂ from the SO₂ laden flue gas 114 and the qualityof the gypsum byproduct 160 produced by the WFGD subsystem. Increasingthe slurry pH by increasing the flow of fresh limestone slurry 141increases the amount of SO₂ removed from the SO₂ laden flue gas 114. Onthe other hand, increasing the flow of limestone slurry 141, and thusthe pH of the slurry 148, slows the SO₂ oxidation after absorption, andthus the transformation of the calcium sulfite to sulfate, which in turnwill result in a lower quality of gypsum 160 being produced.

Thus, there are conflicting control objectives of removing SO₂ from theSO₂ laden flue gas 114, and maintaining the required quality of thegypsum byproduct 160. That is, there may be a conflict between meetingthe SO₂ emission requirements and the gypsum quality requirements.

FIG. 3 details further aspects of the WFGD subsystem described withreference to FIGS. 1 and 2. As shown, SO₂ laden flue gas 114 enters intoa bottom portion of the absorber tower 132 via an aperture 310, and SO₂free flue gas 116 exits from an upper portion of the absorber tower 132via an aperture 312. In this exemplary conventional implementation, acounter current absorber tower is shown, with multiple slurry spraylevels. As shown, the ME wash 200 enters the absorber tower 132 and isdispersed by wash sprayers (not shown).

Also shown are multiple absorber tower slurry nozzles 306A, 306B and306C, each having a slurry sprayer 308A, 308B or 308C, which spraysslurry into the flue gas to absorb the SO₂. The slurry 148 is pumpedfrom the crystallizer 134 shown in FIG. 1, by multiple pumps 133A, 133Band 133C, each of which pumps the slurry up to a different one of thelevels of slurry nozzles 306A, 306B or 306C. It should be understoodthat although 3 different levels of slurry nozzles and sprayers areshown, the number of nozzles and sprayers would vary depending on theparticular implementation.

A ratio of the flow rate of the liquid slurry 148 entering the absorber132 over the flow rate of the flue gas 116 leaving the absorber 132 iscommonly characterized as the L/G. L/G is one of the key designparameters in WFGD subsystems.

The flow rate of the flue gas 116 (saturated with vapor), designated asG, is a function of inlet flue gas 112 from the power generation system110 upstream of the WFGD processing unit 130. Thus, G is not, and cannotbe, controlled, but must be addressed, in the WFGD processing. So, toimpact L/G, the “L” must be adjusted. Adjusting the number of slurrypumps in operation and the “line-up” of these slurry pumps controls theflow rate of the liquid slurry 148 to the WFGD absorber tower 132,designated as L. For example, if only two pumps will be run, running thepumps to the upper two sprayer levels vs. the pumps to top and bottomsprayer levels will create different “L”s.

It is possible to adjust “L” by controlling the operation of the slurrypumps 133A, 133B and 133C. Individual pumps may be turned on or off toadjust the flow rate of the liquid slurry 148 to the absorber tower 132and the effective height at which the liquid slurry 148 is introduced tothe absorber tower. The higher the slurry is introduced into the tower,the more contact time it has with the flue gas resulting in more SO₂removal, but this additional SO₂ removal comes at the penalty ofincreased power consumption to pump the slurry to the higher spraylevel. It will be recognized that the greater the number of pumps, thegreater the granularity of such control.

Pumps 133A-133C, which are extremely large pieces of rotating equipment,can be started and stopped automatically or manually. Most often, in theUSA, these pumps are controlled manually by the subsystem operator. Itis more common to automate starting/stopping rotating equipment, such aspumps 133A-133C in Europe.

If the flow rate of the flue gas 114 entering the WFGD processing unit130 is modified due to a change in the operation of the power generationsystem 110, the WFGD subsystem operator may adjust the operation of oneor more of the pumps 133A-133C. For example, if the flue gas flow ratewere to fall to 50% of the design load, the operator, or special logicin the control system, might shut down one or more of the pumps thatpump slurry to the spray level nozzles at one or more spray level.

Although not shown in FIG. 3, it will be recognized that extra spraylevels, with associated pumps and slurry nozzles, are often provided foruse during maintenance of another pump, or other slurry nozzles and/orslurry sprayers associated with the primary spray levels. The additionof this extra spray level adds to the capital costs of the absorbertower and hence the subsystem. Accordingly, some WFGD owners will decideto eliminate the extra spray level and to avoid this added capitalcosts, and instead add organic acids to the slurry to enhance itsability to absorb and therefore remove SO₂ from the flue gas during suchmaintenance periods. However, these additives tend to be expensive andtherefore their use will result in increased operational costs, whichmay, over time, offset the savings in capital costs.

As indicated in Equation 1 above, to absorb SO₂, a chemical reactionmust occur between the SO₂ in the flue gas and the limestone in theslurry. The result of the chemical reaction in the absorber is theformation of calcium sulfite. In the crystallizer 134, the calciumsulfite is oxidized to form calcium sulfate (gypsum). During thischemical reaction, oxygen is consumed. To provide sufficient oxygen andenhance the speed of the reaction, additional O₂ is added by blowingcompressed air 154 into the liquid slurry in the crystallizer 134.

More particularly, as shown in FIG. 1 ambient air 152 is compressed toform compressed air 154, and forced into the crystallizer 134 by ablower, e.g. fan, 150 in order to oxidize the calcium sulfite in therecycle slurry 148 which is returned from the crystallizer 134 to theabsorber 132 and the gypsum slurry 146 sent to the dewatering system 136for further processing. To facilitate adjustment of the flow ofoxidation air 154, the blower 150 may have a speed or load controlmechanism.

Preferably, the slurry in the crystallizer 134 has excess oxygen.However, there is an upper limit to the amount of oxygen that can beabsorbed or held by slurry. If the O₂ level within the slurry becomestoo low, the chemical oxidation of CaSO₃ to CaSO₄ in the slurry willcease. When this occurs, it is commonly referred to as limestoneblinding. Once limestone blinding occurs, limestone stops dissolvinginto the slurry solution and SO₂ removal can be dramatically reduced.The presence of trace amounts of some minerals can also dramaticallyslow the oxidation of calcium sulfite and/or limestone dissolution tocreate limestone blinding.

Because the amount of O₂ that is dissolved in the slurry is not ameasurable parameter, slurry can become starved for O₂ in conventionalWFGD subsystems if proper precautions are not taken. This is especiallytrue during the summer months when the higher ambient air temperaturelowers the density of the ambient air 152 and reduces the amount ofoxidation air 154 that can be forced into the crystallizer 134 by theblower 150 at maximum speed or load. Additionally, if the amount of SO₂removed from the flue gas flow increases significantly, a correspondingamount of additional O₂ is required to oxidize the SO₂. Thus, the slurrycan effectively become starved for O₂ because of an increase in the flowof SO₂ to the WFGD processing unit.

It is necessary to inject compressed air 154 that is sufficient, withindesign ratios, to oxidize the absorbed SO₂. If it is possible to adjustblower 150 speed or load, and turning down the blower 150 at lower SO₂loads and/or during cooler ambient air temperature periods is desirablebecause it saves energy. When the blower 150 reaches maximum load, orall the O₂ of a non-adjustable blower 150 is being utilized, it is notpossible to oxidize an incremental increase in SO₂. At peak load, orwithout a blower 150 speed control that accurately tracks SO₂ removal,it is possible to create an O₂ shortage in the crystallizer 134.

However, because it is not possible to measure the O₂ in the slurry, thelevel of O₂ in the slurry is not used as a constraint on conventionalWFGD subsystem operations. Thus, there is no way of accuratelymonitoring when the slurry within the crystallizer 134 is becomingstarved for O₂. Accordingly, operators, at best, will assume that theslurry is becoming starved for O₂ if there is a noticeable decrease inthe quality of the gypsum by-product 160, and use their best judgment tocontrol the speed or load of blower 150 and/or decrease SO₂ absorptionefficiency to balance the O₂ being forced into the slurry, with theabsorbed SO₂ that must be oxidized. Hence, in conventional WFGDsubsystems balancing of the O₂ being forced into the slurry with the SO₂required to be absorbed from the flue gas is based, at best, on operatorjudgment.

In summary, conventional control of large WFGD subsystems for utilityapplication is normally carried out within a distributed control system(DCS) and generally consists of on-off control logic as well as FF/PIDfeedback control loops. The parameters controlled are limited to theslurry pH level, the L/G ratio and the flow of forced oxidation air.

The pH must be kept within a certain range to ensure high solubility ofSO₂ (i.e. SO₂ removal efficiency) high quality (purity) gypsum, andprevention of scale buildup. The operating pH range is a function ofequipment and operating conditions. The pH is controlled by adjustingthe flow of fresh limestone slurry 141 to the crystallizer 134. Thelimestone slurry flow adjustment is based on the measured pH of theslurry detected by a sensor. In a typically implementation, a PIDcontroller and, optionally, FF controller included in the DCS arecascaded to a limestone slurry flow controller. The standard/default PIDalgorithm is used for pH control application.

The liquid-to-gas ratio (L/G) is the ratio of the liquid slurry 148flowing to the absorber tower 132 to the flue gas flow 114. For a givenset of subsystem variables, a minimum L/G ratio is required to achievethe desired SO₂ absorption, based on the solubility of SO₂ in the liquidslurry 148. The L/G ratio changes either when the flue gas 114 flowchanges, or when the liquid slurry 148 flow changes, which typicallyoccurs when slurry pumps 133 are turned on or off.

The oxidation of calcium sulfite to form calcium sulfate, i.e. gypsum,is enhanced by forced oxidation, with additional oxygen in the reactiontank of the crystallizer 134. Additional oxygen is introduced by blowingair into the slurry solution in the crystallizer 134. With insufficientoxidation, sulfite—limestone blinding can occur resulting in poor gypsumquality, and potentially subsequent lower SO₂ removal efficiency, and ahigh chemical oxygen demand (COD) in the waste water.

The conventional WFGD process control scheme is comprised of standardcontrol blocks with independent rather than integrated objectives.Currently, the operator, in consultation with the engineering staff,must try to provide overall optimal control of the process. To providesuch control, the operator must take the various goals and constraintsinto account.

Minimized WFGD Operation Costs—Power plants are operated for no otherreason than to generate profits for their owners. Thus, it is beneficialto operate the WFGD subsystem at the lowest appropriate cost, whilerespecting the process, regulatory and byproduct quality constraints andthe business environment.

Maximize SO₂ Removal Efficiency—Clean air regulations establish SO₂removal requirements. WFGD subsystems should be operated to remove SO₂as efficiently as appropriate, in view of the process, regulatory andbyproduct quality constraints and the business environment.

Meet Gypsum Quality Specification—The sale of gypsum as a byproductmitigates WFGD operating costs and depends heavily on the byproductpurity meeting a desired specification. WFGD subsystems should beoperated to produce a gypsum byproduct of an appropriate quality, inview of the process, regulatory and byproduct quality constraints andthe business environment.

Prevent Limestone Blinding—Load fluctuations and variations in fuelsulfur content can cause excursions in SO₂ in the flue gas 114. Withoutproper compensating adjustments, this can lead to high sulfiteconcentrations in the slurry, which in turn results in limestoneblinding, lower absorber tower 132 SO₂ removal efficiency, poor gypsumquality, and a high chemical oxygen demand (COD) in the wastewater. WFGDsubsystems should be operated to prevent limestone binding, in view ofthe process constraints.

In a typical operational sequence, the WFGD subsystem operatordetermines setpoints for the WFGD process to balance these competinggoals and constraints, based upon conventional operating procedures andknowledge of the WFGD process. The setpoints commonly include pH, andthe operational state of the slurry pumps 133 and oxidation air blower150.

There are complex interactions and dynamics in the WFGD process; as aresult, the operator selects conservative operating parameters so thatthe WFGD subsystem is able to meet/exceed hard constraints on SO₂removal and gypsum purity. In making these conservative selections, theoperator often, if not always, sacrifices minimum-cost operation.

For example, FIG. 4 shows SO₂ removal efficiency and gypsum purity as afunction of pH. As pH is increased, the SO₂ removal efficiencyincreases, however, the gypsum purity decreases. Since the operator isinterested in improving both SO₂ removal efficiency and gypsum purity,the operator must determine a setpoint for the pH that is a compromisebetween these competing goals.

In addition, in most cases, the operator is required to meet aguaranteed gypsum purity level, such as 95% purity. Because of thecomplexity of the relationships shown in FIG. 4, the lack of directon-line measurement of gypsum purity, the long time dynamics of gypsumcrystallization, and random variations in operations, the operator oftenchooses to enter a setpoint for pH that will guarantee that the gypsumpurity level is higher than the specified constraint under anycircumstances. However, by guaranteeing the gypsum purity, the operatoroften sacrifices the SO₂ removal efficiency. For instance, based uponthe graph in FIG. 4, the operator may select a pH of 5.4 to guarantee of1% cushion above the gypsum purity constraint of 95%. However, byselecting this setpoint for pH, the operator sacrifices 3% of the SO₂removal efficiency.

The operator faces similar compromises when SO₂ load, i.e. the flue gas114 flow, drops from full to medium. At some point during thistransition, it may be beneficial to shut off one or more slurry pumps133 to save energy, since continued operation of the pump may provideonly slightly better SO₂ removal efficiency. However, because therelationship between the power costs and SO₂ removal efficiency is notwell understood by most operators, operators will typically take aconservative approach. Using such an approach, the operators might notadjust the slurry pump 133 line-up, even though it would be morebeneficial to turn one or more of the slurry pumps 133 off.

It is also well known that many regulatory emission permits provide forboth instantaneous emission limits and some form of rolling-averageemission limits. The rolling-average emission limit is an average of theinstantaneous emissions value over some moving, or rolling, time-window.The time-window may be as short as 1-hour or as long as 1-year. Sometypical time-windows are 1-hour, 3-hours, 8-hours, 24-hours, 1-month,and 1-year. To allow for dynamic process excursions, the instantaneousemission limit is typically higher than rolling average limit. However,continuous operation at the instantaneous emission limit will result ina violation of the rolling-average limit.

Conventionally, the PID 180 controls emissions to the instantaneouslimit, which is relatively simple. To do this, the operating constraintfor the process, i.e. the instantaneous value, is set well within theactual regulatory emission limit, thereby providing a safety margin.

On the other hand, controlling emissions to the rolling-average limit ismore complex. The time-window for the rolling-average is continuallymoving forward. Therefore, at any given time, several time-windows areactive, spanning one time window from the given time back over a periodof time, and another time window spanning from the given time forwardover a period of time.

Conventionally, the operator attempts to control emissions to therolling-average limit, by either simply maintaining a sufficient marginbetween the operating constraint set in the PID 180 for theinstantaneous limit and the actual regulatory emission limit, or byusing operator judgment to set the operating constraint in view of therolling-average limit. In either case, there is no explicit control ofthe rolling-average emissions, and therefore no way to ensure compliancewith the rolling-average limit or prevent costly over-compliance.

Selective Catalytic Reduction System:

Briefly turning to another exemplary air pollution control process, theselective catalytic reduction (SCR) system for NOx removal, similaroperating challenges can be identified. An overview of the SCR processis shown in FIG. 20.

The following process overview is from “Control of Nitrogen OxideEmissions: Selective Catalytic Reduction (SCR)”, Topical Report Number9, Clean Coal Technology, U.S Dept. of Energy, 1997:

Process Overview

NO_(x), which consists primarily of NO with lesser amounts of NO₂, isconverted to nitrogen by reaction with NH₃ over a catalyst in thepresence of oxygen. A small fraction of the SO₂, produced in the boilerby oxidation of sulfur in the coal, is oxidized to sulfur trioxide (SO₃)over the SCR catalyst. In addition, side reactions may produceundesirable by-products: ammonium sulfate, (NH₄)₂SO₄, and ammoniumbisulfate, NH₄HSO₄. There are complex relationships governing theformation of these by-products, but they can be minimized by appropriatecontrol of process conditions.

Ammonia Slip

Unreacted NH₃ in the flue gas downstream of the SCR reactor is referredto as NH₃ slip. It is essential to hold NH₃ slip to below 5 ppm,preferably 2-3 ppm, to minimize formation of (NH₄)₂SO₄ and NH₄HSO₄,which can cause plugging and corrosion of downstream equipment. This isa greater problem with high-sulfur coals, caused by higher SO₃ levelsresulting from both higher initial SO₃ levels due to fuel sulfur contentand oxidation of SO₂ in the SCR reactor.

Operating Temperature

Catalyst cost constitutes 15-20% of the capital cost of an SCR unit;therefore it is essential to operate at as high a temperature aspossible to maximize space velocity and thus minimize catalyst volume.At the same time, it is necessary to minimize the rate of oxidation ofSO₂ to SO₃, which is more temperature sensitive than the SCR reaction.The optimum operating temperature for the SCR process using titanium andvanadium oxide catalysts is about 650-750° F. Most installations use aneconomizer bypass to provide flue gas to the reactors at the desiredtemperature during periods when flue gas temperatures are low, such aslow load operation.

Catalysts

SCR catalysts are made of a ceramic material that is a mixture ofcarrier (titanium oxide) and active components (oxides of vanadium and,in some cases, tungsten). The two leading shapes of SCR catalyst usedtoday are honeycomb and plate. The honeycomb form usually is an extrudedceramic with the catalyst either incorporated throughout the structure(homogeneous) or coated on the substrate. In the plate geometry, thesupport material is generally coated with catalyst. When processing fluegas containing dust, the reactors are typically vertical, with downflowof flue gas. The catalyst is typically arranged in a series of two tofour beds, or layers. For better catalyst utilization, it is common touse three or four layers, with provisions for an additional layer, whichis not initially installed.

As the catalyst activity declines, additional catalyst is installed inthe available spaces in the reactor. As deactivation continues, thecatalyst is replaced on a rotating basis, one layer at a time, startingwith the top. This strategy results in maximum catalyst utilization. Thecatalyst is subjected to periodic soot blowing to remove deposits, usingsteam as the cleaning agent.

Chemistry:

The chemistry of the SCR process is given by the following:4NO+4NH₃+O₂→4N₂+6H₂O2NO₂+4NH₃+O₂→3N₂+6H₂O

The side reactions are given by:SO₂+1/2O₂→SO₃2NH₃+SO₃+H₂O→(NH₄)2SO₄NH₃+SO₃+H₂O→NH₄HSO₄Process Description

As shown in FIG. 20, dirty flue gas 112 leaves the power generationsystem 110. This flue gas may be treated by other air pollution control(APC) subsystems 122 prior to entering the selective catalytic reduction(SCR) subsystem 2170. The flue gas may also be treated by other APCsubsystems (not shown) after leaving the SCR and prior to exiting thestack 117. NOx in the inlet flue gas is measured with one or moreanalyzers 2003. The flue gas with NOx 2008 is passed through the ammonia(NH3) injection grid 2050. Ammonia 2061 is mixed with dilution air 2081by an ammonia/dilution air mixer 2070. The mixture 2071 is dosed intothe flue gas by the injection grid 2050. A dilution air blower 2080supplies ambient air 152 to the mixer 2070, and an ammonia storage andsupply subsystem 2060 supplies the ammonia to the mixer 2070. The NOxladen flue gas, ammonia and dilution air 2055 pass into the SCR reactor2002 and over the SCR catalyst. The SCR catalyst promotes the reductionof NOx with ammonia to nitrogen and water. NOx “free” flue gas 2008leaves the SCR reactor 2002 and exits the plant via potentially otherAPC subsystems (not shown) and the stack 117.

There are additional NOx analyzers 2004 on the NOx “free” flue gasstream 2008 exiting the SCR reactor 2002 or in the stack 117. Themeasured NOx outlet value 2111 is combined with the measured NOx inletvalue 2112 to calculate a NOx removal efficiency 2110. NOx removalefficiency is defined as the percentage of inlet NOx removed from theflue gas.

The calculated NOx removal efficiency 2022 is input to the regulatorycontrol system that resets the ammonia flow rate setpoint 2021A to theammonia/dilution air mixer 2070 and ultimately, the ammonia injectiongrid 2050.

SCR Process Controls

A conventional SCR control system relies on the cascaded control systemshown in FIG. 20. The inner PID controller loop 2010 is used forcontrolling the ammonia flow 2014 into the mixer 2070. The outer PIDcontroller loop 2020 is used for controlling NOx emissions. The operatoris responsible for entering the NOx emission removal efficiency target2031 into the outer loop 2020. As shown in FIG. 21, a selector 2030 maybe used to place an upper constraint 2032 on the target 2031 entered bythe operator. In addition, a feedforward signal 2221 for load (not shownin FIG. 21) is often used so that the controller can adequately handleload transitions. For such implementations, a load sensor 2009 producesa measured load 2809 of the power generation system 110. This measuredload 2809 is sent to a controller 2220 which produces the signal 2221.Signal 2221 is combined with the ammonia flow setpoint 2021A to form anadjusted ammonia flow setpoint 2021B, which is sent to PID controller2010. PID 2010 combines setpoint 2021B with a measured ammonia flow 2012to form an ammonia flow VP 2011 which controls the amount of ammoniasupplied to mixer 2070.

The advantages of this controller are that:

-   -   1. Standard Controller: It is a simple standard controller        design that is used to enforce requirements specified by the SCR        manufacturer and catalyst vendor.    -   2. DCS-Based Controller: The structure is relatively simple, it        can be implemented in the unit's DCS and it is the        least-expensive control option that will enforce equipment and        catalyst operating requirements.        SCR Operating Challenges:

A number of operating parameters affect SCR operation:

-   -   Inlet NOx load,    -   Local molar ratio of NOx:ammonia,    -   Flue gas temperature, and    -   Catalyst quality, availability, and activity.

The operational challenges associated with the control scheme of FIG. 20include the following:

-   -   1. Ammonia Slip Measurement: Maintaining ammonia slip below a        specified constraint is critical to operation of the SCR.        However, there is often no calculation or on-line measurement of        ammonia slip. Even if an ammonia slip measurement is available,        it is often not included directly in the control loop. Thus, one        of the most critical variables for operation of an SCR is not        measured.        -   The operating objective for the SCR is to attain the desired            level of NOx removal with minimal ammonia “slip”. Ammonia            “slip” is defined as the amount of unreacted ammonia in the            NOx “free” flue gas stream. While there is little economic            cost associated with the actual quantity of ammonia in the            ammonia slip, there are significant negative impacts of            ammonia slip:        -   Ammonia can react with SO3 in the flue gas to form a salt,            which deposited on the heat-transfer surfaces of the air            preheater. Not only does this salt reduce the heat-transfer            across the air preheater it also attracts ash that further            reduces the heat-transfer. At a certain point, the            heat-transfer of the air preheater has been reduced to the            point where the preheater must be removed from service for            cleaning (washing). At a minimum, air preheater washing            creates a unit de-rate event.        -   Ammonia is also absorbed in the catalyst (the catalyst can            be considered an ammonia sponge). Abrupt decreases in the            flue gas/NOx load can result in abnormally high short-term            ammonia slip. This is just a transient condition—outside the            scope of the typical control system. While transient in            nature, this slipped ammonia still combines with SO3 and the            salt deposited on the air preheater—even though short-lived,            the dynamic transient can significantly build the salt layer            on the air preheater (and promote attraction of fly ash).        -   Ammonia is also defined as an air pollutant. While ammonia            slip is very low, ammonia is very aromatic, so even            relatively trace amounts can create an odor problem with the            local community.        -   Ammonia is absorbed onto the fly ash. If the ammonia            concentration of the fly ash becomes too great there can be            a significant expensive associated with disposal of the fly            ash.    -   2. NOx Removal Efficiency Setpoint: Without an ammonia slip        measurement, the NOx removal efficiency setpoint 2031 is often        conservatively set by the operator/engineering staff to maintain        the ammonia slip well below the slip constraint. By        conservatively selecting a setpoint for NOx, the        operator/engineer reduces the overall removal efficiency of the        SCR. The conservative setpoint for NOx removal efficiency may        guarantee that an ammonia slip constraint is not violated but it        also results in an efficiency that is lower than would be        possible if the system were operated near the ammonia slip        constraint.    -   3. Temperature Effects on the SCR: With the standard control        system, no attempt is evident to control SCR inlet gas        temperature. Normally some method of ensuring gas temperature is        within acceptable limits is implemented, usually preventing        ammonia injection if the temperature is below a minimum limit.        No attempt to actually control or optimize temperature is made        in most cases. Furthermore, no changes to the NOx setpoint are        made based upon temperature nor based upon temperature profile.    -   4. NOx and Velocity Profile: Boiler operations and ductwork        contribute to create non-uniform distribution of NOx across the        face of the SCR. For minimal ammonia slip, the NOx:ammonia ratio        must be controlled and without uniform mixing, this control must        be local to avoid spots of high ammonia slip. Unfortunately, the        NOx distribution profile is a function of not just the ductwork,        but also boiler operation. So, changes in boiler operation        impact the NOx distribution. Standard controllers do not account        for the fact that the NO_(x) inlet and velocity profiles to the        SCR are seldom uniform or static. This results in over injection        of reagent in some portions of the duct cross section in order        to ensure adequate reagent in other areas. The result is        increased ammonia slip for a given NO_(x) removal efficiency.        Again, the operator/engineer staff often responds to        mal-distribution by lowering the NOx setpoint. It should be        understood that the NOx inlet and outlet analyzers 2003 and 2004        may be a single analyzer or some form of an analysis array. In        addition to the average NOx concentration, a plurality of        analysis values would provide information about the NOx        distribution/profile. To take advantage of the additional NOx        distribution information, it would require a plurality of        ammonia flow controllers 2010 with some intelligence to        dynamically distribute the total ammonia flow among different        regions of the injection grid so that the ammonia flow more        closely matches the local NOx concentration.    -   5. Dynamic Control: The standard controller also fails to        provide effective dynamic control. That is, when the inlet        conditions to the SCR are changing thus requiring modulation of        the ammonia injection rate, it is unlikely that the feedback        control of NO_(x) reduction efficiency will be able to prevent        significant excursions in this process variable. Rapid load        transients and process time delays are dynamic events, which can        cause significant process excursions.    -   6. Catalyst Decay: The catalyst decays over time reducing the        removal efficiency of the SCR and increasing the ammonia slip.        The control system needs to take this degradation into account        in order to maximize NOx removal rate.    -   7. Rolling Average Emissions: Many regulatory emission permits        provide for both instantaneous and some form of rolling-average        emission limits. To allow for dynamic process excursions, the        instantaneous emission limit is higher than rolling average        limit; continuous operation at the instantaneous emission limit        would result in violation of the rolling-average limit. The        rolling-average emission limit is an average of the        instantaneous emissions value over some moving, or rolling,        time-window. The time-window may be as short at 1-hour or as        long a 1-year. Some typical time-windows are 1-hour, 3-hours,        24-hours, 1-month, and 1-year. Automatic control of the rolling        averages is not considered in the standard controller. Most NOx        emission permits are tied back to the regional 8-hour rolling        average ambient air NOx concentration limits.

Operators typically set a desired NOx removal efficiency setpoint 2031for the SCR and make minor adjustments based on infrequent sampleinformation from the fly ash. There is little effort applied toimproving dynamic control of the SCR during load transients or tooptimizing operation of the SCR. Selecting the optimal instantaneous,and if possible, rolling-average NOx removal efficiency is also anelusive and changing problem due to business, regulatory/credit, andprocess issues that are similar to those associated with optimaloperation of the WFGD.

Other APC processes exhibit problems associated with:

-   -   Controlling/optimizing dynamic operation of the process,    -   Control of byproduct/co-product quality,    -   Control of rolling-average emissions, and    -   Optimization of the APC asset.

These problems in other processes are similar to that detailed in theabove discussions of the WFGD and the SCR.

BRIEF SUMMARY OF THE INVENTION

In accordance with the invention, a controller directs the operation ofan air pollution control system performing a process to controlemissions of a pollutant. The air pollution control system could be awet flue gas desulfurization (WFGD) system, a selective catalyticreduction (SCR) system or another type of air pollution control system.The process has multiple process parameters (MPPs), one or more of whichare controllable process parameters (CTPPs), and one of which is anamount of the pollutant (AOP) emitted by the system. A defined AOP value(AOPV) represents an objective or limit on an actual value (AV) of theemitted AOP.

The controller includes a neural network process model or a non-neuralnetwork process model. In either case, the model will represent arelationship between each of at least one CTPP and the emitted AOP. Themodel may, if desired, included a first principle model, a hybrid model,or a regression model. The controller also includes a control processor,which could be or form part of a personal computer (PC) or another typecomputing device, and may sometimes be referred to as a multivariableprocess controller. The control processor is configured with the logic,e.g. software programming or another type of programmed logic, topredict, based on the one model, how changes to a current value of eachof at least one of the CTPPs will affect a future AV of emitted AOP. Theprocessor then selects one of the changes in one CTPP based on thepredicted affect of that change and on the AOPV, and directs control ofthe one CTPP in accordance with the selected change for that CTPP. Ifthe model is a non-neural network process model, the control processormay also have the logic to derive the model based on empirical datarepresenting prior AVs of the MPPs.

Preferably, the controller includes a data storage medium, which couldbe electrical, optical or of some other type, configured to storehistorical data corresponding to prior AVs of the emitted AOP. If so,the control processor may select the one change in the one CTPP basedalso on the stored historical data.

Advantageously, the control processor has the logic to predict, based onthe model, how the changes to the current value of each of the at leastone CTPP will also affect a future value of a non-process parameter,such as parameter associated with the operation of the system to performthe process, e.g. the amount of power or the cost of a reactant used bythe process. In such a case, it may be desirable for the controlprocessor to select the one change in the one CTPP based also on anon-process parameter.

For example, the system might be a wet flue gas desulfurization (WFGD)system that receives SO₂ laden wet flue gas, applies limestone slurry toremove SO₂ from the received SO₂ laden wet flue gas and thereby controlemissions of SO₂, and exhausts desulfurized flue gas. If so, the AOP islikely to be the amount of SO₂ in the exhausted desulfurized flue gas,and the at least one CTPP may include one or more of a parametercorresponding to a pH of the limestone slurry applied and a parametercorresponding to a distribution of the limestone slurry applied.

In some case, the WFGD system will also apply oxidation air tocrystallize the SO₂ removed from the received SO₂ laden wet flue gas andthereby produce gypsum as a by-product of the removal of the SO₂ fromthe received SO₂ laden wet flue gas. In such a case, the at least oneCTPP may include one or more of the parameter corresponding to the pH ofthe limestone slurry applied, the parameter corresponding to thedistribution of the limestone slurry applied, and a parametercorresponding to an amount of the oxidation air applied. The controlprocessor can predict, based on the one model, how changes to thecurrent value of each CTPP will affect a future quality of the producedgypsum by-product. The control processor beneficially will also selectthe one change in the one CTPP based also on a quality constraint on theproduced gypsum by-product.

On the other hand, the system could be a selective catalytic reduction(SCR) system that receives NO_(x) laden flue gas, applies ammonia anddilution air to remove NO_(x) from the received NO_(x) laden flue gasand thereby control emissions of NO_(x), and exhausts reduced NO_(x)flue gas. If so, the AOP will often be the amount of NO_(x) in theexhausted flue gas, and the CTPP to be controlled may be a parametercorresponding to an amount of the ammonia applied. In such a case, thecontrol processor can predict, based on the model, how changes to thecurrent value of that CTPP will affect a future amount of NO_(x) NO_(x)in the exhausted flue gas, and select the one change based on aconstraint on the amount of NO_(x) in the exhausted flue gas.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram depicting an overview of a conventional wetflue gas desulfurization (WFGD) subsystem.

FIG. 2 depicts further details of certain aspects of the WFGD subsystemshown in FIG. 1.

FIG. 3 further details other aspects of the WFGD subsystem shown in FIG.1.

FIG. 4 is a graph of SO₂ removal efficiency vs. gypsum purity as afunction of pH.

FIG. 5A depicts a WFGD constraint box with WFGD process performancewithin a comfort zone.

FIG. 5B depicts the WFGD constraint box of FIG. 5A with WFGD processperformance optimized, in accordance with the present invention.

FIG. 6 depicts a functional block diagram of an exemplary MPC controlarchitecture, in accordance with the present invention.

FIG. 7 depicts components of an exemplary MPC controller and estimatorsuitable for use in the architecture of FIG. 6.

FIG. 8 further details the processing unit and storage disk of the MPCcontroller shown in FIG. 7, in accordance with the present invention.

FIG. 9 depicts a functional block diagram of the estimator incorporatedin the MPC controller detailed in FIG. 8.

FIG. 10 depicts a multi-tier MPCC architecture, in accordance with thepresent invention.

FIG. 11A depicts an interface screen presented by a multi-tier MPCcontroller to the user, in accordance with the present invention.

FIG. 11B depicts another interface screen presented by a multi-tier MPCcontroller for review, modification and/or addition of planned outages,in accordance with the present invention.

FIG. 12 depicts an expanded view of the multi-tier MPCC architecture ofFIG. 10, in accordance with the present invention.

FIG. 13 depicts a functional block diagram of the interfacing of anMPCC, incorporating an estimator, with the DCS for the WFGD process, inaccordance with the present invention.

FIG. 14A depicts a DCS screen for monitoring the MPCC control, inaccordance with the present invention.

FIG. 14B depicts another DCS screen for entering lab and/or othervalues, in accordance with the present invention.

FIG. 15A depicts a WFGD subsystem with overall operations of thesubsystem controlled by an MPCC, in accordance with the presentinvention.

FIG. 15B depicts the MPCC which controls the WFGD subsystem shown inFIG. 15A, in accordance with the present invention.

FIG. 16 depicts further details of certain aspects of the WFGD subsystemshown in FIG. 15A in accordance with the present invention, whichcorrespond to those shown in FIG. 2.

FIG. 17 further details other aspects of the WFGD subsystem shown inFIG. 15A in accordance with the present invention, which correspond tothose shown in FIG. 3.

FIG. 18 further details still other aspects of the WFGD subsystem shownin FIG. 15A in accordance with the present invention.

FIG. 19 further details aspects of the MPCC shown in FIG. 15B, inaccordance with the present invention.

FIG. 20 is a block diagram depicting an overview of a typical selectivecatalytic reduction (SCR) unit.

FIG. 21 depicts the conventional process control scheme for the SCRsubsystem.

FIG. 22 details the processing unit and storage disk of the MPCcontroller in accordance with the present invention.

FIG. 23A depicts a SCR subsystem with overall operations of thesubsystem controlled by an MPCC, in accordance with the presentinvention. FIG. 23B further details aspects of the MPCC shown in FIG.23A, in accordance with the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENT(S) OF THE INVENTION

As demonstrated, efficient and effective operation of WFGD and similarsubsystems is now more complex than ever before. Furthermore, it islikely that this complexity will continue to increase in coming yearswith additional competitive pressures and additional pollutantregulation. Conventional process control strategies and techniques areincapable of dealing with these complexities and hence are incapable ofoptimal control of such operations.

In a business environment that is dynamically changing over the courseof a subsystem's useful operating life, it is desirable to maximize thecommercial value of the subsystem operations at any given time. Thisasset optimization may be based on factors that are not even consideredin the conventional process control strategy. For example, in a businessenvironment in which a market exists for trading regulatory credits,efficient subsystem operation may dictate that additional regulatorycredits can be created and sold to maximize the value of the subsystem,notwithstanding the additional operational costs that may be incurred togenerate such credits.

Thus, rather than a simple strategy of maximizing SO₂ absorption,minimizing operational costs and meeting the byproduct qualityspecification, a more complex strategy can be used to optimize subsystemoperations irrespective of whether or not SO₂ absorption is maximized,operational costs are minimized or the byproduct quality specificationis met. Furthermore, not only can tools be provided to substantiallyimprove subsystem control, such as improved subsystem control can befully automated. Thus, operations can be automated and optimized for notonly operational parameters and constraints, but also the businessenvironment. The subsystem can be automatically controlled to operatevery close to or even precisely at the regulatory permit level, when themarket value of generated regulatory credits is less than the additionaloperational cost for the subsystem to produce such credits. However, thesubsystem can also be automatically controlled to adjust such operationsso as to operate below the regulatory permit level, and thereby generateregulatory credits, when the market value of generated regulatorycredits is greater than the additional operational cost for thesubsystem to produce such credits. Indeed, the automated control candirect the subsystem to operate to remove as much SO₂ as possible up tothe marginal dollar value, i.e. where the value of the emission creditequals processing cost to create the credit.

To summarize, optimized operation of WFGD and similar subsystemsrequires consideration of not only complex process and regulatoryfactors, but also complex business factors, and dynamic changes in thesedifferent types of factors. Optimization may require consideration ofbusiness factors which are local, e.g. one of the multiple WFGDprocessing units being taken off-line, and/or regional, e.g. anotherentity's WFGD processing unit operating within the region being takenoff-line, or even global. Widely and dynamically varying market pricesof, for example, long-term and short-term SO₂ regulatory credits mayalso need to be taken that into account in optimizing operations.

Thus, the controls should preferably be capable of adjusting operationto either minimize SO₂ removal, subject to the regulatory permit, or tomaximum SO₂ removal. The ability to make such adjustments will allow thesubsystem owner to take advantage of a dynamic change in the regulatorycredit value, and to generate credits with one subsystem to offsetout-of-permit operation by another of its subsystems or to takeadvantage of another subsystem owner's need to purchase regulatorycredits to offset out-of-permit operation of that subsystem.Furthermore, the controls should also preferably be capable of adjustingoperations again as soon as the generation of further regulatory creditsis no longer beneficial. Put another way, the control system shouldcontinuously optimize operation of the APC asset subject to equipment,process, regulatory, and business constraints.

Since there is no incentive to exceed the required purify of the gypsumby-product, the controls should preferably facilitate operationaloptimization to match the quality of the gypsum byproduct with thegypsum quality specification or other sales constraint. Optimizedcontrol should facilitate the avoidance of limestone blinding byanticipating and directing actions to adjust the O₂ level in view of thedesired SO₂ absorption level, and gypsum production requirements.

As discussed above, controlling emissions to a rolling-average is acomplex problem. This is because, at least in part, the time-window forthe rolling-average is always moving forward, and at any given time,multiple time-windows are active. Typically, active windows extend fromthe given time to times in the past and other active windows extend fromthe given time to times in the future.

Management of the rolling-average emissions requires integration of allemissions during the time window of the rolling-average. Thus, tooptimize emissions against a rolling-average target requires that aninstantaneous emission target be selected that takes into account theactual past emissions and predicted future emissions or operating plans,for all of the “active” time-windows.

For example, optimization of a four-hour rolling average requires theexamination of multiple time-windows, the first of which starts 3 hoursand 59 minutes in the past and ends at the current time, and the last ofwhich starts at the current time and ends 4 hours into the future. Itshould be recognized that with a one-minute “resolution” of eachtime-window, optimization of this relative-short four-hourrolling-average would involve selecting an instantaneous target thatsatisfies constraints of 479 time-windows.

Determining the rolling-average emission target for a single integratedtime window involves first calculating the total of past emissions inthe integrated time window, and then, for example, predicting a rate offuture emissions for the reminder of that single integrated time windowthat will result in the average emissions during that single integratedtime window being at or under the rolling-average limit. The futureemissions start with the current point in time. However, to be accurate,the future emissions must also include a prediction of the emissionsfrom operations during the reminder of the single integrated timewindow.

It will be understood that the longer the time-window, the moredifficult it is to predict future emissions. For example, emissions fromoperations over the next few hours can be predicted fairly accurately,but the emissions from operations over the next 11 months is moredifficult to predict because factors such as seasonal variation andplanned outages must be taken into account. Additionally, it may benecessary to add a safety margin for unplanned outages or capacitylimitations placed on the subsystem.

Accordingly to optimize the WFGD process, e.g. to minimize theoperational cost and/or maximize SO₂ removal while maintaining theprocess within the operating constraints, optimal setpoints for the WFGDprocess must be automatically determined.

In the embodiments of the invention described in detail below, amodel-based multivariable predictive control (MPC) approach is used toprovide optimal control of the WFGD process. In general, MPC technologyprovides multiple-input, multiple-output dynamic control of processes.As will be recognized by those skilled in the art, MPC technology wasoriginally developed in the later half of the 1970's. Technicalinnovation in the field continues today. MPC encompasses a number ofmodel-based control techniques or methods. These methods allow thecontrol engineer to deal with complex, interacting, dynamic processesmore effectively than is possible with conventional PID type feedbackcontrol systems. MPC techniques are capable of controlling both linearand non-linear processes.

All MPC systems explicitly use dynamic models to predict the processbehavior into the future. A specific control action is then calculatedfor minimizing an objective function. Finally, a receding horizon isimplemented whereby at each time increment the horizon is displaced oneincrement towards the future. Also, at each increment, the applicationof the first control signal, corresponding to the control action of thesequence calculated at that step, is made. There are a number ofcommercial programs available to control engineers such as GeneralizedPredictive Control (GPC), Dynamic Matrix Control (DMC) and Pegasus'Power Perfecter™. Comancho and Bordons provide an excellent overview onthe subject of MPC in Model Predictive Control, Springer-Verlag London,Ltd. 1999, while Lennart Ljund's System Identification, Theory for theUser, Prentice-Hall, Inc. 2^(nd) Edition, 1999, is the classic work onthe dynamic modeling of a process which is necessary to actuallyimplement MPC.

MPC technology is most often used in a supervisory mode to performoperations normally done by the operator rather than replacing basicunderlying regulatory control implemented by the DCS. MPC technology iscapable of automatically balancing competing goals and processconstraints using mathematical techniques to provide optimal setpointsfor the process.

The MPC will typically include such features as:

Dynamic Models: A dynamic model for prediction, e.g. a nonlinear dynamicmodel. This model is easily developed using parametric and step testingof the plant. The high quality of the dynamic model is the key toexcellent optimization and control performance.

Dynamic Identification: Process dynamics, or how the process changesover time, are identified using plant step tests. Based upon these steptests, an optimization-based algorithm is used to identify the dynamicsof the plant.

Steady State Optimization: The steady state optimizer is used to findthe optimal operating point for the process.

Dynamic Control: The dynamic controller is used to compute the optimalcontrol moves around a steady state solution. Control moves are computedusing an optimizer. The optimizer is used to minimize a user specifiedcost function that is subject to a set of constraints. The cost functionis computed using the dynamic model of the process. Based upon themodel, cost function and constraints, optimal control moves can becomputed for the process.

Dynamic Feedback: The MPC controller uses dynamic feedback to update themodels. By using feedback, the effects of disturbances, model mismatchand sensor noise can be greatly reduced.

Advanced Tuning Features: The MPC controller provides a complete set oftuning capabilities. For manipulated variables, the user can set thedesired value and coefficient; movement penalty factor; a lower andupper limit; rate of change constraints; and upper and lower hardconstraints. The user can also use the output of the steady stateoptimizer to set the desired value of a manipulated variable. Forcontrolled variables, the user may set the desired value andcoefficient; error weights; limits; prioritized hard and trajectoryfunnel constraints.

Simulation Environment: An off-line simulation environment is providedfor initial testing and tuning of the controller. The simulationenvironment allows investigation of model mismatch and disturbancerejection capabilities.

On-line System: The MPC control algorithm is preferably implemented in astandardized software server that can be run on a standard commercialoperating system. The server communicates with a DCS through astandardized interface. Engineers and operators may advantageously viewthe output predictions of the MPC algorithm using a graphical userinterface (GUI).

Robust Error Handling: The user specifies how the MPC algorithm shouldrespond to errors in the inputs and outputs. The controller can beturned off if errors occur in critical variables or the last previousknown good value can be used for non-critical variables. By properlyhandling errors, controller up-time operation can be maximized.

Virtual On-Line Analyzers: In cases where direct measurements of aprocess variable are not available, the environment provides theinfrastructure for implementing a software-based virtual on-lineanalyzer (VOA). Using this MPC tool, a model of the desired processvariable may be developed using historical data from the plant,including, if appropriate, lab data. The model can then be fed real-timeprocess variables and predict, in real-time, an unmeasured processvariable. This prediction can then be used in the model predictivecontroller.

Optimizing the WFGD Process

As will be described in more detail below, in accordance with thepresent invention, the SO₂ removal efficiency can be improved. That is,the SO₂ removal rate from the unit can be maximized and/or optimized,while meeting the required or desired constraints, such as a gypsumpurity constraint, instantaneous emissions limit and rolling emissionslimit. Furthermore, operational costs can also or alternatively beminimized or optimized. For example, slurry pumps can be automaticallyturned off when the flue gas load to the WFGD is reduced. Additionally,oxidation air flow and SO₂ removal can also or alternatively bedynamically adjusted to prevent limestone blinding conditions. Using theMPC controller described herein, the WFGD process can be managed closerto the constraints, and achieve enhanced performance as compared toconventionally controlled WFGD processes.

FIGS. 5A and 5B depict WFGD “constraint” boxes 500 and 550. As shown, byidentifying process and equipment constraints 505-520, and usingprocess-based steady-state relationships between multiple independentvariables (MVs) and the identified constraints, i.e. thedependent/controlled variables, it is possible to map the constraintsonto a common “space” in terms of the MVs. This space is actually ann-dimensional space where n is equal to the number of degrees of freedomor number of manipulated MVs in the problem. However, if for purposes ofillustration, we assume that we have two degrees of freedom, i.e. twoMVs, then it is possible to represent the system constraints andrelationships using a two-dimensional (X-Y) plot.

Beneficially the process and equipment constraints bound a non-nullsolution space, which is shown as the areas of feasible operation 525.Any solution in this space will satisfy the constraints on the WFGDsubsystem.

All WFGD subsystems exhibit some degree of variability. Referring toFIG. 5A, the typical conventional operating strategy is to comfortablyplace the normal WFGD subsystem variability within a comfort zone 530 ofthe feasible solution space 525—this will generally ensure safeoperating. Keeping the operations within the comfort zone 530 keeps theoperations away from areas of infeasible/undesirable operation, i.e.away from areas outside the feasible region 525. Typically, distributedcontrol system (DCS) alarms are set at or near the limits of measurableconstraints to alert operators of a pending problem.

While it is true that any point within the feasible space 525 satisfiesthe system constraints 505-520, different points within the feasibilityspace 525 do not have the same operating cost, SO₂ absorption efficiencyor gypsum byproduct production capability. To maximize profit, SO₂absorption efficiency or production/quality of gypsum byproduct, or tominimize cost, requires identifying the economically optimum point foroperation within the feasible space 525.

In accordance with the present invention, the process variables and thecost or benefit of maintaining or changing the values of these variablescan, for example, be used to create an objective function whichrepresents profit, which can in some cases be considered negative cost.As shown in FIG. 5B, using either linear, quadratic or nonlinearprogramming solution techniques, as will described further below, it ispossible to identify an optimum feasible solution point 555, such as theleast-cost or maximum profit solution point within the area of feasibleoperation 525. Since constraints and/or costs can change at any time, itis beneficial to re-identify the optimum feasible solution point 555 inreal time, e.g. every time the MPC controller executes.

Thus, the present invention facilitates the automatic re-targeting ofprocess operation from the conventional operating point within thecomfort zone 530 to the optimum operating point 555, and from optimumoperating point 555 to another optimum operating point when a changeoccurs in the constraints of costs. Once the optimum point isdetermined, the changes required in the values of the MVs to shift theprocess to the optimum operating point, are calculated. These new MVvalues become target values. The target values are steady-state valuesand do not account for process dynamics. However, in order to safelymove the process, process dynamics need to be controlled and managed aswell—which brings us to the next point.

To move the process from the old operating point to the new optimumoperating point, predictive process models, feedback, and high-frequencyexecution are applied. Using MPC techniques, the dynamic path ortrajectory of controlled variables (CVs) is predicted. By using thisprediction and managing manipulated MV adjustments not just at thecurrent time, but also into the future, e.g. the near-term future, it ispossible to manage the dynamic path of the CVs. The new target valuesfor the CVs can be calculated. Then, dynamic error across the desiredtime horizon can also be calculated as the difference between thepredicted path for the CV and the new CV target values. Once again,using optimization theory, an optimum path, which minimizes error, canbe calculated. It should be understood that in practice the engineer ispreferably allowed to weight the errors so that some CVs are controlledmore tightly than others. The predictive process models also allowcontrol of the path or trajectory from one operating point to thenext—so, dynamic problems can be avoided while moving to the new optimumoperating point.

In summary, the present invention allows operations to be conducted atvirtually any point within the area of feasible operation 525 as mightbe required to optimize the process to obtain virtually any desiredresult. That is, the process can be optimized whether the goal is toobtain the lowest possible emissions, the highest quality or quantity ofbyproduct, the lowest operating costs or some other result.

In order to closely approach the optimum operating point 555, the MPCpreferably reduces process variability so that small deviations do notcreate constraint violations. For example, through the use of predictiveprocess models, feedback, and high-frequency execution, the MPC candramatically reduce the process variability of the controlled process.

Steady State and Dynamic Models

As described in the previous paragraphs, a steady state and dynamicmodels are used for the MPC controller. In this section, these modelsare further described.

Steady State Models: The steady state of a process for a certain set ofinputs is the state, which is described by the set of associated processvalues, that the process would achieve if all inputs were to be heldconstant for a long period of time such that previous values of theinputs no longer affect the state. For a WFGD, because of the largecapacity of and relatively slow reaction in the crystallizer in theprocessing unit, the time to steady state is typically on the order of48 hours. A steady state model is used to predict the process valuesassociated with the steady state for a set of process inputs.

First Principles Steady State Model: One approach to developing a steadystate model is to use a set of equations that are derived based uponengineering knowledge of the process. These equations may representknown fundamental relationships between the process inputs and outputs.Known physical, chemical, electrical and engineering equations may beused to derive this set of equations. Because these models are basedupon known principles, they are referred to as first principle models.

Most processes are originally designed using first principle techniquesand models. These models are generally accurate enough to provide forsafe operation in a comfort zone, as described above with reference toFIG. 5A. However, providing highly accurate first principles basedmodels is often time consuming and expensive. In addition, unknowninfluences often have significant effects on the accuracy of firstprinciples models. Therefore, alternative approaches are often used tobuild highly accurate steady state models.

Empirical Models: Empirical models are based upon actual data collectedfrom the process. The empirical model is built using a data regressiontechnique to determine the relationship between model inputs andoutputs. Often times, the data is collected in a series of plant testswhere individual inputs are moved to record their affects upon theoutputs. These plant tests may last days to weeks in order to collectsufficient data for the empirical models.

Linear Empirical Models: Linear empirical models are created by fittinga line, or a plane in higher dimensions, to a set of input and outputdata. Algorithms for fitting such models are commonly available, forexample, Excel provides a regression algorithm for fitting a line to aset of empirical data. Neural Network Models: Neural network models areanother form of empirical models. Neural networks allow more complexcurves than a line to be fit to a set of empirical data. Thearchitecture and training algorithm for a neural network model arebiologically inspired. A neural network is composed of nodes that modelthe basic functionality of a neuron. The nodes are connected by weightswhich model the basic interactions between neurons in the brain. Theweights are set using a training algorithm that mimics learning in thebrain. Using neural network based models, a much richer and complexmodel can be developed than can be achieved using linear empiricalmodels. Process relationships between inputs (Xs) and outputs (Ys) canbe represented using neural network models. Future references to neuralnetworks or neural network models in this document should be interpretedas neural network-based process models.

Hybrid Models: Hybrid models involve a combination of elements fromfirst principles or known relationships and empirical relationships. Forexample, the form of the relationship between the Xs and Y may be known(first principle element). The relationship or equations include anumber of constants. Some of these constants can be determined usingfirst principle knowledge. Other constants would be very difficultand/or expensive to determine from first principles. However, it isrelatively easy and inexpensive to use actual process data for the Xsand Y and the first principle knowledge to construct a regressionproblem to determine the values for the unknown constants. These unknownconstants represent the empirical/regressed element in the hybrid model.The regression is much smaller than an empirical model and empiricalnature of a hybrid model is much less because the model form and some ofthe constants are fixed based on the first principles that govern thephysical relationships.

Dynamic Models: Dynamic models represent the effects of changes in theinputs on the outputs over time. Whereas steady state models are usedonly to predict the final resting state of the process, dynamic modelsare used to predict the path that will be taken from one steady state toanother. Dynamic models may be developed using first principlesknowledge, empirical data or a combination of the two. However, in mostcases, models are developed using empirical data collected from a seriesof step tests of the important variables that affect the state of theprocess.

Pegasus Power Perfecter Model: Most MPC controllers only allow the useof linear empirical models, i.e. the model is composed of a linearempirical steady state model and a linear empirical dynamic model. ThePegasus Power Perfecter™ allows linear, nonlinear, empirical and firstprinciples models to be combined to create the final model that is usedin the controller, and is accordingly preferably used to implement theMPC. One algorithm for combining different types of models to create afinal model for the Pegasus Power Perfecter is described in U.S. Pat.No. 5,933,345.

WFGD Subsystem Architecture

FIG. 6 depicts a functional block diagram of a WFGD subsystemarchitecture with model predictive control. The controller 610incorporates logic necessary to compute real-time setpoints for themanipulated MVs 615, such as pH and oxidation air, of the WFGD process620. The controller 610 bases these computations upon observed processvariables (OPVs) 625, such as the state of MVs, disturbance variables(DVs) and controlled variables (CVs). In addition, a set of referencevalues (RVs) 640, which typically have one or more associated tuningparameters, will also be used in computing the setpoints of themanipulated MVs 615.

An estimator 630, which is preferably a virtual on-line analyzer (VOA),incorporates logic necessary to generate estimated process variables(EPVs) 635. EPV's are typically process variables that cannot beaccurately measured. The estimator 630 implements the logic to generatea real-time estimate of the operating state of the EPVs of the WFGDprocess based upon current and past values of the OPVs. It should beunderstood that the OPVs may include both DCS process measurementsand/or lab measurements. For example, as discussed above the purity ofthe gypsum may be determined based on lab measurements. The estimator630 may beneficially provide alarms for various types of WFGD processproblems.

The controller 610 and estimator 630 logic may be implemented insoftware or in some other manner. It should be understood that, ifdesired, the controller and estimator could be easily implemented withina single computer process, as will be well understood by those skilledin the art.

Model Predictive Control Controller (MPCC)

The controller 610 of FIG. 6 is preferably implemented using a modelpredictive controller (MPCC). The MPCC provides real-timemultiple-input, multiple-output dynamic control of the WFGD process. TheMPCC computes the setpoints for the set of MVs based upon values of theobserved and estimated PVs 625 and 635. A WFGD MPCC may use any of, or acombination of any or all of such values, measured by:

-   -   pH Probes    -   Slurry Density Sensors    -   Temperature Sensors    -   Oxidation-Reduction Potential (ORP) Sensors    -   Absorber Level Sensors    -   SO₂ Inlet and Outlet/Stack Sensors    -   Inlet Flue Gas Velocity Sensors    -   Lab Analysis of Absorber Chemistry (Cl, Mg, Fl)    -   Lab Analysis of Gypsum Purity    -   Lab Analysis of Limestone Grind and Purity

The WFGD MPCC may also use any, or a combination of any or all of thecomputed setpoints for controlling the following:

-   -   Limestone feeder    -   Limestone pulverizers    -   Limestone slurry flow    -   Chemical additive/reactant feeders/valves    -   Oxidation air flow control valves or dampers or blowers    -   pH valve or setpoint    -   Recycle pumps    -   Make up water addition and removal valves/pumps    -   Absorber Chemistry (Cl, Mg, Fl)

The WFGD MPCC may thereby control any, or a combination of any or all ofthe following CVs:

-   -   SO₂ Removal Efficiency    -   Gypsum Purity    -   pH    -   Slurry Density    -   Absorber Level    -   Limestone Grind and Purity    -   Operational Costs

The MPC approach provides the flexibility to optimally compute allaspects of the WFGD process in one unified controller. A primarychallenge in operating a WFGD is to maximize operational profit andminimize operational loss by balancing the following competing goals:

-   -   Maintaining the SO₂ removal rate at an appropriate rate with        respect to the desired constraint limit, e.g. the permit limits        or limits that maximize SO₂ removal credits when appropriate.    -   Maintaining gypsum purity at an appropriate value with respect        to a desired constraint limit, e.g. the gypsum purity        specification limit.    -   Maintaining operational costs at an appropriate level with        respect to a desired limit, e.g. the minimum electrical        consumption costs.

FIG. 7 depicts an exemplary MPCC 700, which includes both a controllerand estimator similar to those described with reference to FIG. 6. Aswill be described further below, the MPCC 700 is capable of balancingthe competing goals described above. In the preferred implementation,the MPCC 700 incorporates Pegasus Power Perfecter™ MPC logic and neuralbased network models, however other logic and non-neural based modelscould instead be utilized if so desired, as discussed above and as willbe well understood by those skilled in the art.

As shown in FIG. 7, MPCC 700 includes a processing unit 705, withmultiple I/O ports 715, and a disk storage unit 710. The disk storage710 unit can be one or more device of any suitable type or types, andmay utilize electronic, magnetic, optical, or some other form or formsof storage media. It will also be understood that although a relativelysmall number of I/O ports are depicted, the processing unit may includeas many or as few I/O ports as appropriate for the particularimplementation. It should also be understood that process data from theDCS and setpoints sent back to the DCS may be packaged together andtransmitted as a single message using standard inter-computercommunication protocols—while the underlying data communicationfunctionality is essential for the operation of the MPCC, theimplementation details are well known to those skilled in the art andnot relevant to the control problem being addressed herein. Theprocessing unit 705 communicates with the disk storage unit 710 to storeand retrieve data via a communications link 712.

The MPCC 700 also includes one or more input devices for accepting userinputs, e.g. operator inputs. As shown in FIG. 7, a keyboard 720 andmouse 725 facilitate the manual inputting of commands or data to theprocessing unit 705, via communication links 722 and 727 and I/O ports715. The MPCC 700 also includes a display 730 for presenting informationto the user. The processing unit 705 communicates the information to bepresented to the user on the display 730 via the communications link733. In addition to facilitating the communication of user inputs, theI/O ports 715 also facilitate the communication of non-user inputs tothe processing unit 705 via communications links 732 and 734, and thecommunication of directives, e.g. generated control directives, from theprocessing unit 715 via communication links 734 and 736.

Processing Unit, Logic and Dynamic Models

As shown in FIG. 8, the processing unit 705 includes a processor 810,memory 820, and an interface 830 for facilitating the receipt andtransmission of I/O signals 805 via the communications links 732-736 ofFIG. 7. The memory 820 is typically a type of random access memory(RAM). The interface 830 facilitates interactions between the processor810 and the user via the keyboard 720 and/or mouse 725, as well asbetween the processor 810 and other devices as will be described furtherbelow.

As also shown in FIG. 8, the disk storage unit 710 stores estimationlogic 840, prediction logic 850, control generator logic 860, a dynamiccontrol model 870, and a dynamic estimation model 880. The stored logicis executed in accordance with the stored models to control of the WFGDsubsystem so as to optimize operations, as will be described in greaterdetail below. The disk storage unit 710 also includes a data store 885for storing received or computed data, and a database 890 formaintaining a history of SO₂ emissions.

A control matrix listing the inputs and outputs that are used by theMPCC 700 to balance the three goals listed above is shown in Table 1below.

TABLE 1 Control Matrix SO₂ Removal Gypsum Purity Operational CostManipulated Variables PH X x Blower Air Amps x X Recycle Pump X X AmpsDisturbance Variables Inlet SO₂ X Flue Gas Velocity X Chloride X xMagnesium X x Fluoride X x Limestone Purity x X and Grind Internal PowerCost X Limestone Cost X Gypsum Price X

In the exemplary implementation described herein, the MPCC 700 is usedto control CVs consisting of the SO₂ removal rate, gypsum purity andoperational costs. Setpoints for MVs consisting of pH level, the load onthe oxidation air blower and the load on the recycle pumps aremanipulated to control the CVs. The MPCC 700 also takes a number of DVsinto account.

The MPCC 700 must balance the three competing goals associated with theCVs, while observing a set of constraints. The competing goals areformulated into an objective function that is minimized using anonlinear programming optimization technique encoded in the MPCC logic.By inputting weight factors for each of these goals, for instance usingthe keyboard 720 or mouse 725, the WFGD subsystem operator or other usercan specify the relative importance of each of the goals depending onthe particular circumstances.

For example, under certain circumstances, the SO₂ removal rate may beweighted more heavily than gypsum purity and operational costs, and theoperational costs may be weighted more heavily than the gypsum purity.Under other circumstances operational costs may be weighted more heavilythan gypsum purity and the SO₂ removal rate, and gypsum purity may beweighted more heavily than the SO₂ removal rate. Under still othercircumstances the gypsum purity may be weighted more heavily than theSO₂ removal rate and operational costs. Any number of weightingcombinations may be specified.

The MPCC 700 will control the operations of the WFGD subsystem based onthe specified weights, such that the subsystem operates at an optimumpoint, e.g. the optimum point 555 shown in FIG. 5B, while stillobserving the applicable set of constraints, e.g. constraints 505-520shown in FIG. 5B.

For this particular example, the constraints are those identified inTable 2 below. These constraints are typical of the type associated withthe CVs and MVs described above.

TABLE 2 Controlled and Manipulated Variable Constraints. Minimum MaximumConstraint Constraint Desired Value Controlled Variables: SO₂ Removal90% 100% Maximize Gypsum Purity 95% 100% Minimize Operation Cost Nonenone Minimize Manipulated Variables: pH 5.0 6.0 computed Blower Air  0%100% computed Recycle Pump #1 Off On computed Recycle Pump #2 Off Oncomputed Recycle Pump #3 Off On computed Recycle Pump #4 Off On computedDynamic Control Model

As noted above, the MPCC 700 requires a dynamic control model 870, withthe input-output structure shown in the control matrix of Table 1. Inorder to develop such a dynamic model, a first principles model and/oran empirical model based upon plant tests of the WFGD process areinitially developed. The first principles model and/or empirical modelscan be developed using the techniques discussed above.

In the case of the exemplary WFGD subsystem under discussion, a steadystate model (first principle or empirical) of the WFGD process for SO₂removal rate and gypsum purity is preferably developed. Using the firstprinciple approach, a steady state model is developed based upon theknown fundamental relationships between the WFGD process inputs andoutputs. Using a neural network approach, a steady state SO₂ removalrate and gypsum purity model is developed by collecting empirical datafrom the actual process at various operating states. A neural networkbased model, which can capture process nonlinearity, is trained usingthis empirical data. It is again noted that although a neural networkbased model may be preferable in certain implementations, the use ofsuch a model is not mandatory. Rather, a non-neural network based modelmay be used if desired, and could even be preferred in certainimplementations.

In addition, the steady state model for operational costs is developedfrom first principles. Simply, costs factors are used to develop a totalcost model. In the exemplary implementation under discussion, the costof various raw materials, such as limestone, and the cost of electricalpower are multiplied by their respective usage amounts to develop thetotal cost model. An income model is determined by the SO₂ removalcredit price multiplied by SO₂ removal tonnage and gypsum pricemultiplied by gypsum tonnage. The operational profit (or loss) can bedetermined by subtracting the cost from the income. Depending on thepump driver (fixed vs. variable speed), optimization of the pump line-upmay involve binary OFF-ON decisions; this may require a secondaryoptimization step to fully evaluate the different pump line-up options.

Even though accurate steady state models can be developed, and could besuitable for a steady state optimization based solution, such models donot contain process dynamics, and hence are not particularly suitablefor use in MPCC 700. Therefore, step tests are performed on the WFGDsubsystem to gather actual dynamic process data. The step-test responsedata is then used to build the empirical dynamic control model 870 forthe WFGD subsystem, which is stored by the processor 810 on the diskstorage unit 710, as shown in FIG. 8.

Dynamic Estimation Model and Virtual On-Line Analyzer

FIG. 6 illustrates how an estimator, such as that incorporated in theMPCC 700, is used in the overall advanced control of the WFGD process.In the MPCC 700, the estimator is preferably in the form of a virtualon-line analyzer (VOA). FIG. 9 further details the estimatorincorporated in the MPCC 700.

As shown in FIG. 9, observed MVs and DVs are input into the empiricaldynamic estimation model 880 for the WFGD subsystem that is used inexecuting the estimation logic 840 on the processor 810. In this regard,the processor 810 executes the estimation logic 840 in accordance withthe dynamic estimation model 880. In this case, estimation logic 840computes current values of the CVs, e.g. SO₂ removal efficiency, gypsumpurity and operational cost.

Table 3 shows the structure for the dynamic estimation model 880. Itshould be noted that the control matrix and dynamic estimation model 880used in the MPCC 700 have the same structure.

TABLE 3 Process model for the estimator. SO₂ Removal Gypsum PurityManipulated Variables PH X x Blower Air Amps x Recycle Pump Amps XDisturbance Variables Inlet SO₂ Flue Gas Velocity Chloride x x Magnesiumx x Fluoride x x Limestone Purity and x Grind

The output of the estimation logic 840 execution is open loop values forSO₂ removal and gypsum purity. The dynamic estimation model 880 for theVOA is developed using the same approach described above to develop thedynamic control model 870. It should be noted that although the dynamicestimation model 880 and dynamic control model 870 are essentially thesame, the models are used for very different purposes. The dynamicestimation model 880 is applied by processor 810 in executing theestimation logic 840 to generate an accurate prediction of the currentvalues of the process variables (PVs), e.g. the estimated CVs 940. Thedynamic control model 870 is applied by the processor 810 in executingthe prediction logic 850 to optimally compute the manipulated MVsetpoints 615 shown in FIG. 6.

As shown in FIG. 9, a feedback loop 930 is provided from the estimationblock 920, which represents the estimated CVs generated by the processor810 as a result of the execution of the estimation logic 840. Thus, thebest estimate of CVs is feed back to the dynamic estimation model 880via the feedback loop 930. The best estimate of CVs from the previousiteration of the estimator is used as starting points for biasing thedynamic estimation model 880 for the current iteration.

The validation block 910 represents a validation of the values ofobserved CVs 950 from, for example, sensor measurements and labanalysis, by the processor 810 using results of the execution of theestimation logic 840, in accordance with the dynamic estimation model880, and observed MVs and DVs 960. The validation represented by block910 is also used to identify potential limestone blinding conditions.For example, if the observed MVs is a pH value measured by one of a pHsensor, the validation 910 of the measured pH based on a pH valueestimated in accordance with the dynamic estimation model 880 mayindicate that the pH sensor is failing. If the observed SO₂ removal,gypsum purity or pH is identified to be in error, the processor 810 willnot use the value in the estimation 920. Rather, a substitute value,preferably the output resulting from the estimation based on the dynamicestimation model, will instead be used. In addition, an alarm may besent to the DCS.

To compute the estimation 920, the processor 810 combines the result ofthe execution of the estimation logic 840 based on the dynamicestimation model 880, with the observed and validated CVs. A Kalmanfilter approach is preferably used for combining the estimation resultwith the observed, validated data. In this case, the validated SO₂removal rate, computed from the inlet and outlet SO₂ sensors, iscombined with the generated removal rate value to produce an estimatedvalue of the true SO₂ removal. Because of the accuracy of the SO₂sensors, the estimation logic 840 preferably places a heavy bias towardsa filtered version of the observed data over the generated value. Gypsumpurity is only measured at most every few hours. The processor 810 willalso combine new observations of gypsum purity with the generatedestimated gypsum purity value. During periods between gypsum samplemeasurements, the processor 810, in accordance with the dynamicestimation model 880, will run open-loop updated estimates of the gypsumpurity based upon changes in the observed MVs and DVs 960. Thus, theprocessor 810 also implements a real-time estimation for the gypsumpurity.

Finally, the processor 810 executes the estimation logic 840, inaccordance with the dynamic estimation model 880, to compute theoperational cost of the WFGD. Since there is no direct on-linemeasurement of cost, the processor 810 necessarily implements thereal-time estimation of the operational costs.

Emissions Management

As discussed above, the operational permits issued in the United Statesgenerally set limits for both instantaneous emissions and therolling-average emissions. There are two classes of rolling-averageemission problems that are beneficially addressed by the MPCC 700 in thecontrol of the WFGD subsystem. The first is class of problem arises whenthe time-window of the rolling-average is less than or equal to thetime-horizon of the prediction logic 850 executed by the processor 810of the MPCC 700. The second class of problem arises when the time-windowof the rolling-average is greater than the time-horizon of theprediction logic 850.

Single Tier MPCC Architecture

The first class of problem, the short time-window problem, is solved byadapting the normal constructs of the MPCC 700 to integrate the emissionrolling-average as an additional CV in the control implemented by theMPCC 700. More particularly, the prediction logic 850 and the controlgenerator logic 860 will treat the steady-state condition as a processconstraint that must be maintained at or under the permit limit, ratherthan as an economic constraint, and will also enforce a dynamic controlpath that maintains current and future values of the rolling-average inthe applicable time-window at or under the permit limit. In this way,the MPCC 700 is provided with a tuning configuration for the emissionrolling-average.

Consideration of Disturbance Variables

Furthermore, DVs for factors such as planned operating events, e.g. loadchanges, that will impact emissions within an applicable horizon areaccounted for in the prediction logic 850, and hence in the MPCC 700control of the WFGD process. In practice, the actual DVs, which arestored as part of the data 885 in the storage disk unit 710, will varybased on the type of WFGD subsystem and the particular operatingphilosophy adopted for the subsystem, e.g. base load vs. swing. The DVscan be adjusted, from time to time, by the operator via inputs enteredusing the keyboard 720 and mouse 725, or by the control generator logic860 itself, or by an external planning system (not shown) via theinterface 830.

However, the DVs are typically not in a form that can be easily adjustedby operators or other users. Therefore, an operational plan interfacetool is preferably provided as part of the prediction logic 850 to aidthe operator or other user in setting and maintaining the DVs.

FIGS. 11A and 11B depict the interface presented on the display 730 forinputting a planned outage. As shown in FIG. 11A a screen 1100 ispresented which displays the projected power generation system runfactor and the projected WFGD subsystem run factor to the operator orother user. Also displayed are buttons allowing the user to input one ormore new planned outages, and to display previously input plannedoutages for review or modification.

If the button allowing the user to input a planned outage is selectedusing the mouse 725, the user is presented with the screen 1110 shown inFIG. 11B. The user can then input, using the keyboard 720 variousdetails regarding the new planned outage as shown. By clicking on theadd outage button provided, the new planned outage is added as a DV andaccounted for by the prediction logic 850. The logic implementing thisinterface sets the appropriate DVs so that the future operating plan iscommunicated to the MPCC processing unit 705.

Whatever the actual DVs, the function of the DVs will be the same, whichis to embed the impact of the planned operating events into theprediction logic 850, which can then be executed by the MPCC processor810 to predict future dynamic and steady-state conditions of therolling-average emission CV. Thus, the MPCC 700 executes the predictionlogic 850 to compute the predicted emission rolling-average. Thepredicted emission rolling average is in turn used as an input to thecontrol generator logic 860, which is executed by the MPCC processor 810to account for planned operating events in the control plan. In thisway, the MPCC 700 is provided with a tuning configuration for theemission rolling-average in view of planned operating events, andtherefore with the capability to control the operation of the WFGDwithin the rolling-average emission permit limit notwithstanding plannedoperating events.

Two Tier MPCC Architecture

The second class of problem, the long time-window problem, isbeneficially addressed using a two-tiered MPCC approach. In thisapproach the MPCC 700 includes multiple, preferably two, cascadedcontroller processors.

Referring now to FIG. 10, a tier 1 controller processing unit (CPU) 705Aoperates to solve the short-term, or short time-window problem, in themanner described above with reference to the single tier architecture.As shown in FIG. 10, the CPU 705A includes a processor 810A. Theprocessor 810A executes prediction logic 850A stored at disk storageunit 710A to provide dynamic rolling-average emission management withina time-window equal to the short term of applicable time horizon. A CVrepresenting the short term or applicable control horizonrolling-average emission target is stored as part of the data 885A inthe storage device unit 710A of the CPU 705A.

The CPU 705A also includes memory 820A and interface 830A similar tomemory 820 and interface 830 described above with reference to FIG. 8.The interface 830A receives a subset of the MPCC 700 I/O signals, i.e.I/O signals 805A. The storage disk unit 710A also stores the estimationlogic 840A and dynamic estimation model 880A, the control generatorlogic 860A and dynamic control model 870A, and the SO₂ emissions historydatabase 890A, all of which are described above with reference to FIG.8. The CPU 705A also includes a timer 1010, typically a processor clock.The function of the timer 1010 will be described in more detail below.

The tier 2 CPU 705B operates to solve the long-term, or long time-windowproblem. As shown in FIG. 10, the CPU 705B includes a processor 810B.The processor 810B executes prediction logic 850B to also providedynamic rolling-average emission management. However, the predictionlogic 850B is executed to manage the dynamic rolling-average emission inview of the full future time-window of the rolling-average emissionconstraint, and to determine the optimum short-term or applicable timehorizon, rolling-average emission target, i.e. the maximum limit, forthe tier 1 CPU 705A. Accordingly, the CPU 705B serves as a long-termrolling average emission optimizer and predicts the emission rollingaverage over the applicable time horizon for control of the emissionrolling-average over the full future time window.

The CV representing the long term time horizon rolling-average emissionconstraint is stored as part of the data 885B in the disk storage unit710B. The CPU 705B also includes memory 820B and interface 830B, similarto memory 820 and interface 830 described above. The interface 830Breceives a subset of the MPCC 700 I/O signals, i.e. I/O signals 805B.

Although the two-tier architecture in FIG. 10 includes multiple CPUs, itwill be recognized that the multi-tier prediction logic can, if desired,be implemented in other ways. For example, in FIG. 10, tier 1 of theMPCC 700 is represented by CPU 705A, and tier 2 of the MPCC 700 isrepresented by CPU 705B. However, a single CPU, such as CPU 705 of FIG.8, could be used to execute both prediction logic 850A and predictionlogic 850B, and thereby determine the optimum short-term or applicabletime horizon rolling-average emission target, in view of the predictedoptimum the long-term rolling average emission to solve the long-term,or long time-window problem, and to optimize the short-term orapplicable term rolling average emission in view of the determinedtarget.

As noted above, the CPU 705B looks to a long-term time horizon,sometimes referred to as the control horizon, corresponding to thetime-window of the rolling average. Advantageously, CPU 705B manages thedynamic rolling-average emission in view of the full future time-windowof the rolling-average emission, and determines the optimum short-termrolling-average emission limit. The CPU 705B executes at a frequencyfast enough to allow it to capture changes to the operating plan overrelatively short periods.

The CPU 705B utilizes the short-term or applicable term rolling averageemission target, which is considered a CV by CPU 705A, as an MV, andconsiders the long term emission rolling average a CV. The long termemission rolling average is therefore stored as part of the data 885B indisk storage unit 710B. The prediction logic 850B will treat thesteady-state condition as a process constraint that must be maintainedat or under the permit limit, rather than as an economic constraint, andwill also enforce a dynamic control path that maintains current andfuture values of the rolling-average in the applicable time-window at orunder the permit limit. In this way, the MPCC 700 is provided with atuning configuration for the emission rolling-average.

Furthermore, DVs for factors such planned operating events, e.g. loadchanges, that will impact emissions within an applicable horizon areaccounted for in the prediction logic 850B, and hence in the MPCC 700control of the WFGD process. As noted above, in practice the actual DVs,which are stored as part of the data 885B in the storage disk 710B, willvary based on the type of WFGD subsystem and the particular operatingphilosophy adopted for the subsystem, and can be adjusted by theoperator, or by the CPU 705B executing the control generator logic 860B,or by an external planning system (not shown) via the interface 830B.However, as discussed above, the DVs are typically not in a form thatcan be easily adjusted by operators or other users, and therefore anoperational plan interface tool, such as that shown in FIGS. 11A and11B, is preferably provided as part of the prediction logic 850A and/or850B to aid the operator or other user in setting and maintaining theDVs.

However, here again, whatever the actual DVs, the function of the DVswill be the same, which is to embed the impact of the planned operatingevents into the prediction logic 850B, which can then be executed by theMPCC processor 810B to predict future dynamic and steady-stateconditions of the long term rolling-average emission CV.

Thus, the CPU 705B executes the prediction logic 850B to determine theoptimum short-term or applicable term rolling-average emission limit inview of the planned operating events in the control plan. The optimumshort-term or applicable term rolling-average emission limit istransmitted to CPU 705A via communications link 1000. In this way, theMPCC 700 is provided with a tuning configuration for optimizing theemission rolling-average in view of planned operating events, andtherefore with the capability to optimize control of the operation ofthe WFGD within the rolling-average emission permit limitnotwithstanding planned operating events.

FIG. 12 depicts an expanded view of the multi-tier MPCC architecture. Asshown, an operator or other user utilizes a remote control terminal 1220to communicate with both a process historian database 1210 and the MPCC700 via communications links 1225 and 1215. The MPCC 700 includes CPU705A and CPU 705B of FIG. 10, which are interconnected via thecommunications link 1000. Data associated with the WFGD process istransmitted, via communications link 1230, to the process historiandatabase 1210, which stores this data as historical process data. Asfurther described further below, necessary stored data is retrieved fromthe database 1210 via communications link 1215 and processed by CPU705B. Necessary data associated with the WFGD process is alsotransmitted, via communications link 1235 to, and processed by CPU 705A.

As previously described, the CPU 705A receives CV operating targetscorresponding to the current desired long term rolling average targetfrom CPU 705B via communications link 1000. The communicated rollingaverage target is the optimized target for the long-term rolling averagegenerated by the CPU 705B executing the prediction logic 850B. Thecommunications between CPU 705A and CPU 705B are handled in the samemanner as communications between an MPC controller and a real-timeoptimizer.

CPU 705A and CPU 705B beneficially have a handshaking protocol whichensures that if CPU 705B stops sending optimized targets for thelong-term rolling average to CPU 705A, CPU 705A will fall-back, or shed,to an intelligent and conservative operating strategy for the long-termrolling average constraint. The prediction logic 850A may include a toolfor establishing such a protocol, thereby ensuring the necessaryhandshaking and shedding. However, if the prediction logic 850A does notinclude such a tool, the typical features and functionality of the DCScan be adapted in a manner well known to those skilled in the art, toimplement the required handshaking and shedding.

The critical issue is to ensure that CPU 705A is consistently using atimely, i.e. fresh—not stale, long-term rolling average target. Eachtime CPU 705B executes the prediction logic 850B, it will calculate afresh, new, long-term rolling average target. CPU 705A receives the newtarget from CPU 705B via communications link 1000. Based on receipt ofthe new target, CPU 705A executes the prediction logic 850A to re-setthe timer 1010. If CPU 705A fails to timely receive a new target fromCPU 705B via communications link 1000, the timer 1010 times out, orexpires. Based on the expiration of the timer 1010, CPU 750A, inaccordance with the prediction logic, considers the current long-termrolling average target to be stale and sheds back to a safe operatingstrategy until it receives a fresh new long-term rolling average targetfrom CPU 705B.

Preferably, the minimum timer setting is a bit longer than the executionfrequency of CPU 705B to accommodate computer load/scheduling issues.Due to the non-scheduled operation of many real-time optimizers, it iscommon conventional practice to set the communications timers at a halfto two times the time to steady-state of a controller. However, sinceexecution of the prediction logic by CPU 705B is scheduled, therecommended guideline for setting timer 1010 is not that of asteady-state optimization link, but should, for example, be no more thantwice the execution frequency of the controller running on CPU 705 Bplus about 3 to 5 minutes.

If CPU 705A determines that the current long-term rolling average targetis stale and sheds, the long-term rolling average constraint must bereset. Without CPU 705B furnishing a fresh new long-term rolling averagetarget, CPU 705A has no long-term guidance or target. Accordingly, insuch a case CPU 705A increases the safety margin of process operations.

For example, if the rolling-average period is relatively short, e.g. 4to 8 hours, and the subsystem is operating under base-load conditions,CPU 705A might increase the stale rolling average removal target, by 3to 5 weight percent, in accordance with the prediction logic 850A. Suchan increase should, under such circumstances, establish a sufficientsafety margin for continued operations. With respect to operator inputnecessary to implement the increase, all that is required is entry of asingle value, e.g. 3 weight percent, to the prediction logic.

On the other hand, if the rolling-average period is relative long, e.g.24 or more hours, and/or the subsystem is operating under a non-constantload, the CPU 705A might shed back to a conservative target, inaccordance with the prediction logic 850A. One way this can be done isfor CPU 705A to use an assumed constant operation at or above theplanned subsystem load across the entire period of the rolling averagetime window. The CPU 705A can then calculate, based on such constantoperation, a constant emission target and add a small safety margin orcomfort factor that can be determined by site management. To implementthis solution in CPU 705A, the prediction logic 850A must include thenoted functionality. It should, however, be recognized that, if desired,the functionality to set this conservative target could be implementedin the DOS rather than the CPU 705A. It would also be possible toimplement the conservative target as a secondary CV in the tier 1controller 705A and only enable this CV when the short-term rollingaverage target 1200 is stale.

Thus, whether the rolling-average period is relative short or longand/or the subsystem is operating under a constant or non-constant load,preferably the prediction logic 850A includes the shed-limits, so thatoperator action is not required. However, other techniques could also beemployed to establish a shed limit—so long as the technique establishessafe/conservative operation with respect to the rolling averageconstraint during periods when the CPU 705B is not providing fresh, new,long-term rolling average targets.

It should be noted that actual SO₂ emissions are tracked by the MPCC 700in the process historian database 1210 whether or not the CPU 705B isoperating properly or furnishing fresh, new, long-term rolling averagetargets to CPU 705A. The stored emissions can therefore be used by CPU705B to track and account for SO₂ emissions that occur even when CPU705B is not operating or communicating properly with CPU 705A. However,after the CPU 705B is once again operating and capable of communicatingproperly, it will, in accordance with the prediction logic 850B,re-optimize the rolling average emissions and increase or decrease thecurrent rolling average emission target being utilized by CPU 705A toadjust for the actual emissions that occurred during the outage, andprovide the fresh, new, long-term rolling average target to CPU 705A viacommunications link 1000.

On-Line Implementation

FIG. 13 depicts a functional block diagram of the interfacing of an MPCC1300 with the DCS 1320 for the WFGD process 620. The MPCC 1300incorporates both a controller 1305, which may be similar to controller610 of FIG. 6, and an estimator 1310, which may be similar to estimator630 of FIG. 6. The MPCC 1300 could, if desired, be the MPCC shown inFIGS. 7 and 8. The MPCC 1300 could also be configured using a multi-tierarchitecture, such as that shown in FIGS. 10 and 12.

As shown, the controller 1305 and estimator 1310 are connected to theDCS 1320 via a data Interface 1315, which could be part of the interface830 of FIG. 8. In this preferred implementation, the data interface 1315is implemented using a Pegasus(™) Data Interface (PDI) software module.However, this is not mandatory and the data interface 1315 could beimplemented using some other interface logic. The data interface 1315sends setpoints for manipulated MVs and read PVs. The setpoints may besent as I/O signals 805 of FIG. 8.

In this preferred implementation, the controller 1305 is implementedusing the Pegasus(™) Power Perfecter (PPP), which is composed of threesoftware components: the data server component, the controller componentand the graphical user interface (GUI) component. The data servercomponent is used to communicate with PDI and collect local data relatedto the control application. The controller component executes theprediction logic 850 to perform model predictive control algorithmiccalculations in view of the dynamic control model 870. The GUI componentdisplays, e.g. on display 730, the results of these calculations andprovides an interface for tuning the controller. Here again, the use ofthe Pegasus(™) Power Perfecter is not mandatory and the controller 1305could be implemented using some other controller logic.

In this preferred implementation, the estimator 1310 is implementedusing the Pegasus(™) Run-time Application Engine (RAE) software module.The RAE communicates directly with the PDI and the PPP. The RAE isconsidered to provide a number of features that make it a verycost-effective environment to host the VOA. Functionality for errorchecking logic, heartbeat monitoring, communication and computer processwatchdog capability, and alarming facilities are all beneficiallyimplemented in the RAE. However, once again, the use of the Pegasus(™)Run-time Application Engine is not mandatory and the estimator 1315could be implemented using some other estimator logic. It is alsopossible, as will be recognized by those skilled in the art, toimplement a functionally equivalent VOA in the DCS for the WFGD 620, ifso desired.

The controller 1305, estimator 1310 and PDI 1315 preferably execute onone processor, e.g. processor 810 of FIG. 8 or 810A of FIG. 10, that isconnected to a control network including the DCS 1320 for the WFGDprocess 620, using an Ethernet connection. Presently, it is typicallythat the processor operating system be Microsoft Windows™ based,although this is not mandatory. The processor may also be part of highpower workstation computer assembly or other type computer, as forexample shown in FIG. 7. In any event, the processor, and its associatedmemory must have sufficient computation power and storage to execute thelogic necessary to perform the advanced WFGD control as describedherein.

DCS Modifications

As described above with reference to FIG. 13, the controller processorexecuting the prediction logic 850 interfaces to the DCS 1320 for theWFGD process 620 via interface 1315. To facilitate proper interfacing ofthe controller 1305 and DCS 1320, a conventional DCS will typicallyrequire modification. Accordingly, the DCS 1320 is beneficially aconventional DCS that has been modified, in a manner well understood inthe art, such that it includes the features described below.

The DCS 1320 is advantageously adapted, i.e. programmed with thenecessary logic typically using software, to enable the operator orother user to perform the following functions from the DCS interfacescreen:

-   -   Change the CONTROL MODE of the PPP between auto and manual.    -   View the CONTROLLER STATUS.    -   View status of WATCHDOG TIMER (“HEARTBEAT”).    -   View MV attributes for STATUS, MIN, MAX, CURRENT VALUE.    -   ENABLE each MV or turn each MV to off.    -   View CV attributes for MIN, MAX, and CURRENT value.    -   Enter lab values for gypsum purity, absorber chemistry and        limestone characteristics.

As an aid for user access to this functionality, the DCS 1320 is adaptedto display two new screens, as shown in FIGS. 14A and 14B. The screen1400 in FIG. 14A is used by the operator or other user to monitor theMPCC control and the screen 1450 in FIG. 14B is used by the operator orother user to enter lab and/or other values as may be appropriate.

For convenience and to avoid complexity unnecessary to understanding theinvention, items such as operational costs are excluded from the controlmatrix for purposes of the following description. However, it will beunderstood that operational costs are easily, and may in many cases bepreferably, included in the control matrix. In addition for convenienceand to simplify the discussion, recycle pumps are treated as DVs ratherthan MVs. Here again, those skilled in the art will recognize that, inmany cases, it may be preferable to treat the recycle pumps as MVs.Finally, it should be noted that in the following discussion it isassumed that the WFGD subsystem has two absorber towers and twoassociated MPCCs (one instance of the MPCC for each absorber in the WFGDsubsystem).

Advanced Control DCS Screens

Referring now to FIG. 14A, as shown the screen 1400 includes aCONTROLLER MODE that is an operator/user-selected tag that can be inauto or manual. In AUTO, the controller 1305 executing the predictionlogic 850, e.g. Pegasus(™) Power Perfecter, computes MV movements andexecutes the control generator logic 860 to direct control signalsimplementing these movements to the DCS 1320. The controller 1305executing the prediction logic 850 will not calculate MV moves unlessthe variable is enabled, i.e. is designated AUTO.

The controller 1305 executing prediction logic 850, such as Pegasus(™)Power Perfecter, includes a watchdog timer or “heartbeat” function thatmonitors the integrity of the communications interface 1315 with the DCS1320. An alarm indicator (not shown) will appear on the screen if thecommunications interface 1315 fails. The controller 1305 executingprediction logic 850 will recognize an alarm status, and based on thealarm status will initiate shedding of all enabled, i.e. active,selections to a lower level DCS configuration.

The screen 1400 also includes a PERFECTER STATUS, which indicateswhether or not the prediction logic 850 has been executed successfullyby the controller 1305. A GOOD status (as shown) is required for thecontroller 1305 to remain in operation. The controller 1305 executingprediction logic 850 will recognize a BAD status and, responsive torecognizing a BAD status, will break all the active connections, andshed, i.e. return control to the DCS 1320.

As shown, MVs are displayed with the following information headings:

ENABLED—This field can be set by an operator or other user input to thecontroller 1305 executing prediction logic 850, to enable or disableeach MV. Disabling the MV corresponds to turning the MV to an offstatus.

SP—Indicates the prediction logic 850 setpoint.

MODE—Indicates whether prediction logic 850 recognizes the applicable MVas being on, on hold, or completely off.

MIN LMT—Displays the minimum limit being used by the prediction logic850 for the MV. It should be noted that preferably these values cannotbe changed by the operator or other user.

MAX LMT—Displays the maximum limit being used by the prediction logic850 for the MV. Here again, preferably these values cannot be changed.

PV—Shows the latest or current value of each MV as recognized by theprediction logic 850.

The screen 1400 further includes details of the MV status fieldindicators as follows:

The controller 1305 executing prediction logic 850 will only adjust aparticular MV if it's MODE is ON. Four conditions must be met for thisto occur. First, the enable box must be selected by the operator orother user. The DCS 1320 must be in auto mode. The shed conditions mustbe false, as computed by the controller 1305 executing prediction logic850. Finally, hold conditions must be false, as computed by thecontroller 1305 executing prediction logic 850.

The controller 1305 executing prediction logic 850 will change anddisplay an MV mode status of HOLD if conditions exist that will notallow controller 1305 to adjust that particular MV. When in HOLD status,the controller 1305, in accordance with the prediction logic 850, willmaintain the current value of the MV until it is able to clear the holdcondition. For the MV status to remain in HOLD, four conditions must besatisfied. First, the enable box must be selected by the operator orother user. The DCS 1320 must be in auto mode. The shed conditions mustbe false, as computed by the controller 1305 executing prediction logic850. Finally, the hold conditions must be true, as computed by thecontroller 1305 executing prediction logic 850.

The controller 1305 executing prediction logic 850 will change the MVmode status to off, and display on off mode status, if conditions existthat will not allow controller 1305 to adjust that particular MV basedon any of the following conditions. First, the enable box for thecontrol mode is deselected by the operator or other user. The DCS modeis not in auto, e.g. is in manual. Any shed condition is true, ascomputed by the controller 1305 executing prediction logic 850.

The controller 1305, executing prediction logic 850, will recognizevarious shed conditions, including the failure of the estimator 1310 toexecute and the failure to enter lab values during a predefined priorperiod, e.g. in last 12 hours. If the controller 1305, executingprediction logic 850, determines that any of the above shed conditionsare true, it will return control of the MV to the DCS 1320.

As also shown in FIG. 14A, CVs are displayed with the followinginformation headings:

PV—Indicates the latest sensed value of the CV received by thecontroller 1305.

LAB—Indicates the latest lab test value along with time of the samplereceived by the controller 1305.

ESTIMATE—Indicates the current or most recent CV estimate generated bythe estimator 1310, executing the estimation logic 840 based on thedynamic estimation model.

MIN—Displays the minimum limit for the CV.

MAX—Displays the maximum limit for the CV.

In addition, the screen 1400 displays trend plots over somepredetermined past period of operation, e.g. over the past 24 hours ofoperation, for the estimated values of the CVs.

Lab Sample Entry Form

Referring now to FIG. 14B, a prototype Lab Sample Entry Form DCS screen1450 is displayed to the operator or other user. This screen can be usedby the operator or other user to enter the lab sample test values thatwill be processed by the estimator 1310 of FIG. 13, in accordance withthe estimation logic 840 and dynamic estimation model 880, as previouslydescribed with reference to FIG. 8.

As shown in FIG. 14B, the following values are entered along with anassociated time stamp generated by the estimator 1310:

Unit 1 Lab Sample Values:

-   -   Gypsum Purity    -   Chloride    -   Magnesium    -   Fluoride

Unit 2 Lab Sample Values:

-   -   Gypsum Purity    -   Chloride    -   Magnesium    -   Fluoride

Unit 1 and Unit 2 Combined Lab Sample Values:

-   -   Gypsum Purity    -   Limestone Purity    -   Limestone Grind

The operator or other user enters the lab test values along with theassociated sample time, for example using the keyboard 720 shown in FIG.7. After entry of these values, the operator will activate the updatebutton, for example using the mouse 725 shown in FIG. 7. Activation ofthe update button will cause the estimator 1310 to update the values forthese parameters during the next execution of the estimation logic 840.It should be noted that, if desired, these lab test values couldalternatively be automatically fed to the MPCC 1300 from the applicablelab in digitized form via the interface of the MPCC processing unit,such as the interface 830 shown in FIG. 8. Furthermore, the MPCC logiccould be easily adapted, e.g. programmed, to automatically activate theupdate function represented by the update button responsive to thereceipt of the test values in digitized form from the applicable lab orlabs.

To ensure proper control of the WFGD process, lab test values for gypsumpurity should be updated every 8 to 12 hours. Accordingly, if the purityis not updated in that time period, the MPCC 1300 is preferablyconfigured, e.g. programmed with the necessary logic, to shed controland issue an alarm.

In addition, absorber chemistry values and limestone characteristicvalues should be updated at least once a week. Here again, if thesevalues are not updated on time, the MPCC 1300 is preferably configuredto issue an alarm.

Validation logic is included in the estimation logic 840 executed by theestimator 1310 to validate the operator input values. If the values areincorrectly input, the estimator 1310, in accordance with the estimationlogic 840, will revert to the previous values, and the previous valueswill continue to be displayed in FIG. 14B and the dynamic estimationmodel will not be updated.

Overall WFGD Operations Control

The control of the overall operation of a WFGD subsystem by an MPCC, ofany of the types discussed above, will now be described with referencesto FIGS. 15A, 15B, 16, 17, 18 and 19.

FIG. 15A depicts a power generation system (PGS) 110 and air pollutioncontrol (APC) system 120 similar to that described with reference toFIG. 1, with like reference numerals identifying like elements of thesystems, some of which may not be further described below to avoidunnecessary duplication.

As shown, the WFGD subsystem 130′ includes a multivariable control,which in this exemplary implementation is performed by MPCC 1500, whichmay be similar to MPCC 700 or 1300 describe above and which, if desired,could incorporate a multi-tier architecture of the type described withreference to FIGS. 10-12.

Flue gas 114 with SO₂ is directed from other APC subsystems 122 to theabsorber tower 132. Ambient air 152 is compressed by a blower 150 anddirected as compressed oxidation air 154′ to the crystallizer 134. Asensor 1518 detects a measure of the ambient conditions 1520. Themeasured ambient conditions 1520 may, for example, include temperature,humidity and barometric pressure. The blower 150 includes a blower loadcontrol 1501 which is capable of providing a current blower load value1502 and of modifying the current blower load based on a received blowerload SP 1503.

As also shown, limestone slurry 148′, is pumped by slurry pumps 133 fromthe crystallizer 134 to the absorber tower 132. Each of the slurry pumps133 includes a pump state control 1511 and pump load control 1514. Thepump state control 1511 is capable of providing a current pump statevalue 1512, e.g. indicating the pump on/off state, and of changing thecurrent state of the pump based on a received pump state SP 1513. Thepump load control 1514 is capable of providing a current pump load value1515 and of changing the current pump load based on a pump load SP 1516.The flow of fresh limestone slurry 141′ from the mixer & tank 140 to thecrystallizer 134 is controlled by a flow control valve 199 based on aslurry flow SP 196′. The slurry flow SP 196′ is based on a PID controlsignal 181′ determined based on a pH SP 186′, as will be discussedfurther below. The fresh slurry 141′ flowing to the crystallizer 134serves to adjust the pH of the slurry used in the WFGD process, andtherefore to control the removal of SO₂ from the SO₂ laden flue gas 114entering the absorber tower 132.

As has been previously discussed above, the SO₂ laden flue gas 114enters the base of the absorber tower 132. SO₂ is removed from the fluegas 114 in the absorber tower 132. The clean flue gas 116′, which ispreferably free of SO₂, is directed from the absorber tower 132 to, forexample the stack 117. An SO₂ analyzer 1504, which is shown to be at theoutlet of the absorber tower 132 but could be located at the stack 117or at another location downstream of the absorber tower 132, detects ameasure of the outlet SO₂ 1505.

On the control side of the subsystem 130′, the multivariable processcontroller for the WFGD process, i.e. MPCC 1500 shown in FIG. 15B,receives various inputs. The inputs to the MPCC 1500 include themeasured slurry pH 183, measured inlet SO₂ 189, the blower load value1502, the measured outlet SO₂ 1505, the lab tested gypsum purity value1506, the measured PGS load 1509, the slurry pump state values 1512, theslurry pump load values 1515, and the measured ambient conditions values1520. As will be described further below, these process parameterinputs, along with other inputs including non-process inputs 1550 andconstraint inputs 1555, and computed estimated parameter inputs 1560,are used by the MPCC 1500 to generate controlled parameter setpoints(SPs) 1530.

In operation, SO₂ analyzer 188, located at or upstream of the WFGDabsorber tower 132, detects a measure of the inlet SO₂ in the flue gas114. The measured value 189 of the inlet SO₂ is fed to the feed forwardunit 190 and MPCC 1500. The load of the power generation system (PGS)110 is also detected by a PGS load sensor 1508 and fed, as measured PGSload 1509, to the MPCC 1500. Additionally, SO₂ analyzer 1504 detects ameasure of the outlet SO₂ in the flue gas leaving the absorber tower132. The measured value 1505 of the outlet SO₂ is also fed to the MPCC1500.

Estimating Gypsum Quality

Referring now also to FIG. 19, the parameters input to the MPCC 1500include parameters reflecting the ongoing conditions within the absorbertower 132. Such parameters can be use by the MPCC 1500 to generate andupdate a dynamic estimation model for the gypsum. The dynamic estimationmodel for the gypsum could, for example, form a part of dynamicestimation model 880.

As there is no practical way to directly measure gypsum purity on-line,the dynamic gypsum estimation model can be used, in conjunction withestimation logic executed by the estimator 1500B of MPCC 1500, such asestimation logic 840, to compute an estimation of the gypsum quality,shown as calculated gypsum purity 1932. The estimator 1500B ispreferably a virtual on-line analyzer (VOA). Although the controller1500A and estimator 1500B are shown to be housed in a single unit, itwill be recognized that, if desired, the controller 1500A and estimator1500B could be housed separately and formed of separate components, solong as the controller 1500A and estimator 1500B units were suitablylinked to enable the required communications. The computed estimation ofthe gypsum quality 1932 may also reflect adjustment by the estimationlogic based on gypsum quality lab measurements, shown as the gypsumpurity value 1506, input to the MPCC 1500.

The estimated gypsum quality 1932 is then passed by the estimator 1500Bto the controller 1500A of the MPCC 1500. The controller 1500A uses theestimated gypsum quality 1932 to update a dynamic control model, such asdynamic control model 870. Prediction logic, such as prediction logic850, is executed by the controller 1500A, in accordance with the dynamiccontrol model 870, to compare the adjusted estimated gypsum quality 1932with a gypsum quality constraint representing a desired gypsum quality.The desired gypsum quality is typically established by a gypsum salescontract specification. As shown, the gypsum quality constraint is inputto the MPCC 1500 as gypsum purity requirement 1924, and is stored asdata 885.

The controller 1500A, executing the prediction logic, determines if,based on the comparison results, adjustment to the operation of the WFGDsubsystem 130′ is required. If so, the determined difference between theestimated gypsum quality 1932 and the gypsum quality constraint 1924 isused by the prediction logic being executed by the controller 1500A, todetermine the required adjustments to be made to the WFGD subsystemoperations to bring the quality of the gypsum 160′ within the gypsumquality constraint 1924.

Maintaining Compliance with Gypsum Quality Requirements

To bring the quality of the gypsum 160′ into alignment with the gypsumquality constraint 1924, the required adjustments to the WFGDoperations, as determined by the prediction logic, are fed to controlgenerator logic, such as control generator logic 860, which is alsoexecuted by controller 1500A. Controller 1500A executes the controlgenerator logic to generate control signals corresponding to requiredincrease or decrease in the quality of the gypsum 160′.

These control signals might, for example, cause an adjustment to theoperation of one or more of valve 199, the slurry pumps 133 and theblower 150, shown in FIG. 15A, so that a WFGD subsystem processparameter, e.g. the measured pH value of slurry 148′ flowing from thecrystallizer 134 to the absorber tower 132, which is represented bymeasured slurry pH value 183 detected by pH sensor 182 in FIG. 15A,corresponds to a desired setpoint (SP), e.g. a desired pH value. Thisadjustment in the pH value 183 of the slurry 148′ will in turn result ina change in the quality of the gypsum byproduct 160′ actually beingproduce by WFGD subsystem 130′, and in the estimated gypsum quality 1932computed by the estimator 1500B, to better correspond to the desiredgypsum quality 1924.

Referring now also to FIG. 16, which further details the structure andoperation of the fresh water source 164, mixer/tank 140 and dewateringunit 136. As shown, the fresh water source 164 includes a water tank164A from which an ME wash 200 is pumped by pump 164B to the absorbertower 132 and a fresh water source 162 is pumped by pump 164C to themixing tank 140A.

Operation and control of the dewatering unit 136 is unchanged byaddition of the MPCC 1500.

Operation and control of the limestone slurry preparation area,including the grinder 170 and the Mixer/Tank 140, are unchanged byaddition of the MPCC 1500.

Referring now to FIGS. 15A, 15B and 16, the controller 1500A may, forexample, execute the control generator logic to direct a change in theflow of limestone slurry 141′ to the crystallizer 134. The volume ofslurry 141′ that flows to the crystallizer 134, is controlled by openingand closing valve 199. The opening and closing of the valve 199 iscontrolled by PID 180. The operation of the PID 180 to control theoperation of the valve 199 is based on an input slurry pH setpoint.

Accordingly, to properly control the flow of slurry 141′ to thecrystallizer 134, the controller 1500A determines the slurry pH setpointthat will bring the quality of the gypsum 160′ into alignment with thegypsum quality constraint 1924. As shown in FIGS. 15A and 16, thedetermined slurry pH setpoint, shown as pH SP 186′, is transmitted tothe PID 180. The PID 180 then controls the operation of valve 199 tomodify the slurry flow 141′ to correspond with the received pH SP 186′.

To control the operation of valve 199, the PID 180 generates a PIDcontrol signal 181′, based on the received slurry pH SP 186′ and thereceived pH value 183 of the slurry 141′ measured by the pH sensor 182.The PID control signal 181′ is combined with the feed forward (FF)control signal 191, which is generated by the FF unit 190. As is wellunderstood in the art, the FF control signal 191 is generated based onthe measured inlet SO₂ 189 of the flue gas 114, received from an SO₂analyzer 188 located upstream of the absorber tower 132. PID controlsignal 181′ and (FF) control signal 191 are combined at summation block192, which is typically included as a built-in feature in the DCS outputblock that communicates to the valve 199. The combined control signalsleaving the summation block 192 are represented by the slurry flowsetpoint 196′.

The slurry flow setpoint 196′ is transmitted to valve 199.Conventionally, the valve 199 valve includes another PID (not shown)which directs the actual opening or closing of the valve 199 based onthe received slurry flow setpoint 196′, to modify the flow of slurry141′ through the valve. In any event, based on the received slurry flowsetpoint 196′, the valve 199 is opened or closed to increase ordecreases the volume of slurry 141′, and therefore the volume of slurry141′, flowing to the crystallizer 134, which in turn modifies pH of theslurry in the crystallizer 134 and the quality of the gypsum 160′produced by the WFGD subsystem 130′.

Factors to be considered in determining when and if the MPCC 1500 is toreset/update the pH setpoint at the PID 180 and/or the PID 180 is toreset/update the limestone slurry flow setpoint at the valve 199 can beprogrammed, using well know techniques, into the MPCC 1500 and/or PID180, as applicable. As is well understood by those skilled in the art,factors such as the performance of PID 180 and the accuracy of the pHsensor 182 are generally considered in such determinations.

The controller 1500A generates the pH SP 186′ by processing the measuredpH value of the slurry 148′ flowing from the crystallizer 134 to theabsorber tower 132 received from the pH sensor 182, represented byslurry pH 183, in accordance with a gypsum quality control algorithm orlook-up table, in the dynamic control model 870. The algorithm orlook-up table represents an established linkage between the quality ofthe gypsum 160′ and the measured pH value 183.

The PID 180 generates the PID control signal 181′ by processing thedeference between the pH SP 186′ received from the controller 1500A andthe measured pH value of the slurry 148′ received from the pH sensor182, represented by slurry pH 183, in accordance with a limestone flowcontrol algorithm or look-up table. This algorithm or look-up tablerepresents an established linkage between the amount of change in thevolume of the slurry 141′ flowing from the mixer/tank 140 and the amountof change in the measured pH value 183 of the slurry 148′ flowing fromthe crystallizer 134 to the absorber tower 132. It is perhaps worthwhileto note that although in the exemplary embodiment shown in FIG. 16, theamount of ground limestone 174 flowing from the grinder 170 to themixing tank 140A is managed by a separate controller (not shown), ifbeneficial this could also be controlled by the MPCC 1500. Additionally,although not shown the MPCC 1500 could, if desired, also control thedispensing of additives into the slurry within the mixing tank 140AAccordingly, based on the received pH SP 186′ from the controller 1500Aof the MPCC 1500, the PID 180 generates a signal, which causes the valve199 to open or close, thereby increasing or decreasing the flow of thefresh limestone slurry into the crystallizer 134. The PID continuescontrol of the valve adjustment until, the volume of limestone slurry141′ flowing through the valve 199 matches the MVSP represented by thelimestone slurry flow setpoint 196′. It will be understood thatpreferably the matching is performed by a PID (not shown) included aspart of the valve 199. However, alternatively, the match could beperformed by the PID 180 based on flow volume data measured andtransmitted back from the valve.

Maintaining Compliance with SO₂ Removal Requirements

By controlling the pH of the slurry 148′, the MPCC 1500 can control theremoval of SO₂ from the SO₂ laden flue gas 114 along with the quality ofthe gypsum byproduct 160′ produced by the WFGD subsystem. Increasing thepH of the slurry 148′ by increasing the flow of fresh limestone slurry141′ through valve 199 will result in the amount of SO₂ removed by theabsorber tower 132 from the SO₂ laden flue gas 114 being increased. Onthe other hand, decreasing the flow limestone slurry 141′ through valve199 decreases the pH of the slurry 148′. Decreasing the amount ofabsorbed SO₂ (now in the form of calcium sulfite) flowing to thecrystallizer 134 will also will result in a higher percentage of thecalcium sulfite being oxidized in the crystallizer 134 to calciumsulfate, hence yielding a higher gypsum quality.

Thus, there are is a tension between two primary control objectives, thefirst being to remove the SO₂ from the SO₂ laden flue gas 114, and thesecond being to produce a gypsum byproduct 160′ having the requiredquality. That is, there may be a control conflict between meeting theSO₂ emission requirements and the gypsum specification.

Referring now also to FIG. 17, which further details the structure andoperation of the slurry pumps 133 and absorber tower 132. As shown, theslurry pumps 133 include multiple separate pumps, shown as slurry pumps133A, 133B and 133C in this exemplary embodiment, which pump the slurry148′ from the crystallizer 134 to the absorber tower 132. As previouslydescribed with reference to FIG. 3, each of the pumps 133A-133C directsslurry to a different one of the multiple levels of absorber towerslurry level nozzles 306A, 306B and 306C. Each of the slurry level306A-306C, directs slurry to a different one of the multiple levels ofslurry sprayers 308A, 308B and 308C. The slurry sprayers 308A-308C spraythe slurry, in this case slurry 148′, into the SO₂ laden flue gas 114,which enters the absorber tower 132 at the gas inlet aperture 310, toabsorb the SO_(2,). The clean flue gas 116′ is then exhausted from theabsorber tower 132 at the absorber outlet aperture 312. As alsopreviously described, an ME spray wash 200 is directed into the absorbertower 132. It will be recognized that although 3 different levels ofslurry nozzles and sprayers, and three different pumps, are shown, thenumber of levels of nozzles and sprayers and the number of pumps can andin all likelihood will very depending on the particular implementation.

As shown in FIG. 15A, the pump state values 1512 are fed back from apump state controls 1511, such as on/off switches, and pump load values1515 are fed back from pump load controls 1514, such as a motor, to theMPCC 1500 for input to the dynamic control model. As also shown, thepump state setpoints 1513, such as a switch on or off instructions, arefed to the pump state controls 1511, and pump load setpoints 1516 arefed to the pump load controls 1514 by the MPCC 1500 to control thestate, e.g. on or off, and load of each of pumps 133A-133C, and therebycontrol which levels of nozzles the slurry 148′ is pumped to and theamount of slurry 148′ that is pumped to each level of nozzles. It shouldbe recognized that in most current WFGD applications, the slurry pumps133 do not include variable load capabilities Oust On/Off), so the pumpload setpoints 1516 and load controls 1514 would not be available foruse or adjustment by the MPCC 1500.

As detailed in the exemplary implementation depicted in FIG. 17, pumpstate controls 1511 include an individual pump state control for eachpump, identified using reference numerals 1511A, 1511B and 1511C.Likewise, pump load controls 1514 include an individual pump statecontrol for each pump, identified using reference numerals 1514A, 1514Band 1514C. Individual pump state values 1512A, 1512B, and 1512C are fedto MPCC 1500 from pump state controls 1511A, 1511B, and 1511C,respectively, to indicate the current state of that slurry pump.Similarly, individual pump load values 1515A, 1515B, and 1515C are fedto MPCC 1500 from pump load controls 1514A, 1514B, and 1514C,respectively, to indicate the current state of that slurry pump. Basedon the pump state values 1512A, 1512B, and 1512C, the MPCC 1500,executes the prediction logic 850, to determine the current state ofeach of pumps 133A, 133B and 133C, and hence what is commonly referredto as the pump line-up, at any given time.

As discussed previously above, a ratio of the flow rate of the liquidslurry 148′ entering the absorber tower 132 over the flow rate of theflue gas 114 entering the absorber tower 132, is commonly characterizedas the L/G. L/G is one of the key design parameters in WFGD subsystems.Since the flow rate of the flue gas 114, designated as G, is setupstream of the WFGD processing unit 130′, typically by the operation ofthe power generation system 110, it is not, and cannot be, controlled.However, the flow rate of the liquid slurry 148′, designated as L, canbe controlled by the MPCC 1500 based on the value of G.

One way in which this is done is by controlling the operation of theslurry pumps 133A, 133B and 133C. Individual pumps are controlled by theMPCC 1500, by issuing pump state setpoints 1513A, 1513B and 1513C to thepump state controls 1511A of pump 133A, 1511B of pump 133B and 1511C ofpump 133C, respectively, to obtain the desired pump line-up, and hencethe levels at which slurry 148′ will enter the absorber tower 132. Ifavailable in the WFGD subsystem, the MPCC 1500 could also issues pumpload control setpoints 1516A, 1516B and 1516C to the pump load controls1514A of pump 133A, 1514B of pump 133B and 1514C of pump 133C,respectively, to obtain a desired volume of flow of slurry 148′ into theabsorber tower 132 at each active nozzle level. Accordingly, the MPCC1500 controls the flow rate, L, of the liquid slurry 148′ to theabsorber tower 132 by controlling which levels of nozzles 306A-306C theslurry 148′ is pumped to and the amount of slurry 148′ that is pumped toeach level of nozzles. It will be recognized that the greater the numberof pumps and levels of nozzles, the greater the granularity of suchcontrol.

Pumping slurry 148′ to higher level nozzles, such as nozzles 306A, willcause the slurry, which is sprayed from slurry sprayers 308A, to have arelatively long contact period with the SO₂ laden flue gas 114. Thiswill in turn result in the absorption of a relatively larger amount ofSO₂ from the flue gas 114 by the slurry than slurry entering theabsorber at lower spray levels. On the other hand, pumping slurry tolower level nozzles, such as nozzles 306C, will cause the slurry 148′,which is sprayed from slurry sprayers 308C, to have a relatively shortercontact period with the SO₂ laden flue gas 114. This will result in theabsorption of a relatively smaller amount of SO₂ from the flue gas 114by the slurry. Thus, a greater or lesser amount of SO₂ will be removedfrom the flue gas 114 with the same amount and composition of slurry148′, depending on the level of nozzles to which the slurry is pumped.

However, to pump the liquid slurry 148′ to higher level nozzles, such asnozzles 306A, requires relative more power, and hence greateroperational cost, than that required to pump the liquid slurry 148′ tolower level nozzles, such as nozzles 306C. Accordingly, by pumping moreliquid slurry to higher level nozzles to increase absorption and thusremoval of sulfur from the flue gas 114, the cost of operation of theWFGD subsystem are increased.

Pumps 133A-133C are extremely large pieces of rotating equipment. Thesepumps can be started and stopped automatically by the MPCC 1500 byissuing pump state SPs, or manually by the subsystem operator or otheruser. If the flow rate of the flue gas 114 entering the absorber tower132 is modified due to a change in the operation of the power generationsystem 110, MPCC 1500, executing the prediction logic 850, in accordancewith the dynamic control model 870, and the control generator logic 860,will adjust the operation of one or more of the slurry pumps 133A-133C.For example, if the flue gas flow rate were to fall to 50% of the designload, the MPCC might issue one or more pump state SPs to shut down, i.e.turn off, one or more of the pumps currently pumping slurry 148′ to theabsorber tower nozzles at one or more of the spray levels, and/or one ormore pump load control SPs to reduce the pump load of one or more of thepumps currently pumping slurry to the absorber tower nozzles at one ormore spray level.

Additionally, if a dispenser (not shown) for organic acid or the like isincluded as part of the mixer/pump 140 or as a separate subsystem thatfed the organic acid directly to the process, the MPCC 1500 might alsoor alternatively issue control SP signals (not shown) to reduce theamount of organic acid or other like additive being dispensed to theslurry to reduce the ability of the slurry to absorb and thereforeremove SO₂ from the flue gas. It will be recognized that these additivestend to be quite expensive, and therefore their use has been relativelylimited, at least in the United States of America. Once again, there isa conflict between SO₂ removal and operating cost: the additives areexpensive, but the additives can significantly enhance SO₂ removal withlittle to no impact on gypsum purity. If the WFGD subsystem includes anadditive injection subsystem, it would therefore be appropriate to allowthe MPCC 1500 to control the additive injection in concert with theother WFGD process variables such that the MPCC 1500 operates the WFGDprocess at the lowest possible operating cost while still withinequipment, process, and regulatory constraints. By inputting the cost ofsuch additives to the MPCC 1500, this cost factor can be included in thedynamic control model and considered by the executing prediction logicin directing the control of the WFGD process.

Avoiding Limestone Binding

As previously discussed, in order to oxidize the absorbed SO₂ to formgypsum, a chemical reaction must occur between the SO₂ and the limestonein the slurry in the absorber tower 132. During this chemical reaction,oxygen is consumed to form the calcium sulfate. The flue gas 114entering the absorber tower 132 is O₂ poor, so additional O₂ istypically added into the liquid slurry flowing to the absorber tower132.

Referring now also to FIG. 18, a blower 150, which is commonlycharacterized as a fan, compresses ambient air 152. The resultingcompressed oxidation air 154′ is directed to the crystallizer 134 andapplied to the slurry within the crystallizer 134 which will be pumpedto the absorber 132, as has been previously discussed with reference toFIG. 17. The addition of the compressed oxidation air 154′ to the slurrywithin the crystallizer 134 results in the recycled slurry 148′, whichflows from the crystallizer 134 to the absorber 132 having an enhanceoxygen content which will facilitate oxidization and thus the formationof calcium sulfate.

Preferably, there is an excess of oxygen in the slurry 148′, although itwill be recognized that there is an upper limit to the amount of oxygenthat can be absorbed or held by slurry. To facilitate oxidation, it isdesirable to operate the WFGD with a significant amount of excess O₂ inthe slurry.

It will also be recognized that if the O₂ concentration within theslurry becomes too low, the chemical reaction between the SO₂ in theflue gas 114 and the limestone in the slurry 148′ will slow andeventually cease to occur. When this occurs, it is commonly referred toas limestone blinding.

The amount of O₂ that is dissolved in the recyclable slurry within thecrystallizer 134 is not a measurable parameter. Accordingly, the dynamicestimation model 880 preferably includes a model of the dissolved slurryO₂. The estimation logic, e.g. estimation logic 840 executed by theestimator 1500B of MPCC 1500, in accordance with the dynamic estimationmodel 880, computes an estimate of the dissolved O₂ in the recyclableslurry within the crystallizer 134. The computed estimate is passed tocontroller 1500A of MPCC 1500, which applies the computed estimate toupdate the dynamic control model, e.g. dynamic control model 870. Thecontroller 1500A then executes the prediction logic, e.g. predictionlogic 850, which compares the estimated dissolved slurry O₂ value with adissolved slurry O₂ value constraint, which has been input to MPCC 1500.The dissolved slurry O₂ value constraint is one of the constraints 1555shown in FIG. 15B, and is depicted more particularly in FIG. 19 as thedissolved slurry O₂ requirement 1926.

Based on the result of the comparison, the controller 1500A, stillexecuting the prediction logic, determines if any adjustment to theoperations of the WFGD subsystem 130′ is required in order to ensurethat the slurry 148′ which is pumped to the absorber tower 132 does notbecome starved for O₂. It will be recognized that ensuring that theslurry 148′ has a sufficient amount of dissolved O₂, also aids inensuring that the SO₂ emissions and the quality of the gypsum by-productcontinue to meet the required emissions and quality constraints.

As shown in FIGS. 15A and 18, the blower 150 includes a load controlmechanism 1501, which is sometimes referred to as a blower speed controlmechanism, which can adjust the flow of oxidation air to thecrystallizer 134. The load control mechanism 1501 can be used to adjustthe load of the blower 150, and thus the amount of compressed oxidationair 154′ entering the crystallizer 134, and thereby facilitate anyrequired adjustment to the operations of the WFGD subsystem 130′ in viewof the comparison result. Preferably, the operation of the load controlmechanism 1501 is controlled directly by the controller 1500A. However,if desired, the load control mechanism 1501 could be manually controlledby a subsystem operator based on an output from the controller 1500Adirecting the operator to undertake the appropriate manual control ofthe load control mechanism. In either case, based on the result of thecomparison, the controller 1500A executes the prediction logic 850, inaccordance with the dynamic control model 870, to determine if anadjustment to the amount of compressed oxidation air 154′ entering thecrystallizer 134 is required to ensure that the slurry 148′ being pumpedto the absorber tower 132 does not become starved for O₂ and, if so, theamount of the adjustment. The controller 1500A then executes controlgenerator logic, such as control generator logic 860, in view of theblower load value 1502 received by the MPCC 1500 from the load controlmechanism 1501, to generate control signals for directing the loadcontrol mechanism 1501 to modify the load of the blower 150 to adjustthe amount of compressed oxidation air 154′ entering the crystallizer134 to a desired amount that will ensure that the slurry 148′ beingpumped to the absorber tower 132 does not become starved for O₂.

As has been noted previously, O₂ starvation is particularly of concernduring the summer months when the heat reduces the amount of compressedoxidation air 154′ that can be forced into the crystallizer 134 by theblower 150. The prediction logic 850 executed by the controller 1500Amay, for example, determine that the speed or load of blower 150, whichis input to the MPCC 1500 as the blower load value 1502, should beadjusted to increase the volume of compressed oxidation air 154′entering the crystallizer 134 by a determined amount. The controlgenerator logic executed by the controller 1500A then determines theblower load SP 1503 which will result in the desired increase the volumeof compressed oxidation air 154′. Preferably, the blower load SP 1503 istransmitted from the MPCC 1500 to the load control mechanism 1501, whichdirects an increase in the load on the blower 150 corresponding to theblower load SP 1503, thereby avoiding limestone blinding and ensuringthat the SO₂ emissions and the quality of the gypsum by-product arewithin the applicable constraints.

Increasing the speed or load of the blower 150 will of course alsoincrease the power consumption of the blower, and therefore theoperational costs of the WFGD subsystem 130′. This increase in cost isalso preferably monitored by the MPCC 1500 while controlling theoperations of the WFGD subsystem 130′, and thereby provide an economicincentive for controlling the blower 150 to direct only the necessaryamount of compressed oxidation air 154′ into the crystallizer 134.

As shown in FIG. 19, the current cost/unit of power, depicted as unitpower cost 1906, is preferably input to the MPCC 1500 as one of thenon-process inputs 1550 shown in FIG. 15B, and included in the dynamiccontrol model 870. Using this information, the controller 1500A of theMPCC 1500 can also compute and display to the subsystem operator orothers the change in the cost of operation based on the adjustment ofthe flow of compressed oxidation air 154′ to the crystallizer 134.

Accordingly, provided that there is excess blower 150 capacity, thecontroller 1500A will typically control the flow of compressed oxidationair 154′ to the crystallizer 134 to ensure that it is sufficient toavoid binding. However, if the blower 150 is operating at full load andthe amount of compressed oxidation air 154′ flowing to the crystallizer134 is still insufficient to avoid binding, i.e. addition air (oxygen)is needed for oxidation of all the SO₂ being absorbed in absorber tower132, the controller 1500A will need to implement an alternative controlstrategy. In this regard, once the SO₂ is absorbed into the slurry, itmust be oxidized to gypsum—however, if there is no additional oxygen tooxidize the marginal SO₂, then it is best not to absorb the SO₂ becauseif the absorbed SO₂ can not be oxidized, limestone blinding willeventually occur.

Under such circumstances, the controller 1500A has another option whichcan be exercised in controlling the operation of the WFGD subsystem130′, to ensure that binding does not occur. More particularly, thecontroller 1500A, executing the prediction logic 850 in accordance withthe dynamic control model 870 and the control generator logic 860, cancontrol the PID 180 to adjust the pH level of the slurry 141′ flowing tothe crystallizer 134, and thereby control the pH level of the slurry148′ being pumped to the absorber tower 132. By directing a decrease inthe pH level of the slurry 148′ being pumped to the absorber tower 132,the additional marginal SO₂ absorption will be reduced and binding canbe avoided.

Still another alternative strategy which can be implemented by thecontroller 1500A, is to operate outside of the constraints 1555 shown inFIG. 15B. In particular, the controller 1500A could implement a controlstrategy under which not as much of the SO₂ in the slurry 148′ in thecrystallizer 134 is oxidized. Accordingly the amount of O₂ required inthe crystallizer 134 will be reduced. However, this action will in turndegrade the purity of the gypsum byproduct 160′ produced by the WFGDsubsystem 130′. Using this strategy, the controller 1500A overrides oneor more of the constraints 1555 in controlling the operation of the WFGDsubsystem 130′. Preferably, the controller maintains the hard emissionconstraint on SO₂ in the clean flue gas 116′, which is depicted asoutlet SO₂ permit requirement 1922 in FIG. 19, and overrides, andeffectively lowers the specified purity of the gypsum byproduct 160′,which is depicted as gypsum purity requirement 1924 in FIG. 19.

Accordingly, once the maximum blower capacity limit has been reached,the controller 1500A may control the operation of the WFGD subsystem130′ to decrease pH of the slurry 148′ entering the absorber tower 132and thereby reduce SO₂ absorption down to the emission limit, i.e.outlet SO₂ permit requirement 1922. However, if any further reduction inSO₂ absorption will cause a violation of the outlet SO₂ permitrequirement 1922 and there is insufficient blower capacity to providethe needed amount of air (oxygen) to oxidize all of the absorbed SO₂that must be removed, the physical equipment, e.g. the blower 150 and/orcrystallizer 134, is undersized and it is not possible to meet both theSO₂ removal requirement and the gypsum purity. Since the MPCC 1500cannot “create” the required additional oxygen, it must consider analternate strategy. Under this alternate strategy, the controller 1500Acontrols the operation of the WFGD subsystem 130′ to maintain a currentlevel of SO₂ removal, i.e. to meet the outlet SO₂ permit requirement1922, and to produce gypsum meeting a relaxed gypsum purity constraint,i.e. meeting a gypsum purity requirement which is less than the inputgypsum purity requirement 1924. Beneficially the controller 1500Aminimizes the deviation between the reduced gypsum purity requirementand the desired gypsum purity requirement 1924. It should be understoodthat a still further alternative is for the controller 1500A to controlthe operation of the WFGD subsystem 130′ in accordance with a hybridstrategy which implements aspects of both of the above. Thesealternative control strategies can be implemented by setting standardtuning parameters in the MPCC 1500.

MPCC Operations

As has been described above, MPCC 1500 is capable of controlling largeWFGD subsystems for utility applications within a distributed controlsystem (DCS). The parameters which can be controlled by the MPCC 1500are virtually unlimited, but preferably include at least one or more of:(1) the pH of the slurry 148′ entering the absorber tower 132, (2) theslurry pump line-up that delivers liquid slurry 148′ to the differentlevels of the absorber tower 132, and (3) the amount of compressedoxidation air 154′ entering the crystallizer 134. As will be recognized,it is the dynamic control model 870 that contains the basic processrelationships that will be utilized by the MPCC 1500 to direct controlof the WFGD process. Accordingly, the relationships established in thedynamic control model 870 are of primary importance to the MPCC 1500. Inthis regard, the dynamic control model 870 relates various parameters,such as the pH and oxidation air levels, to various constraints, such asthe gypsum purity and SO₂ removal levels, and it is these relationshipswhich allow the dynamic and flexible control of the WFGD subsystem 130′as will be further detailed below.

FIG. 19 depicts, in greater detail, the preferred parameters andconstraints that are input and used by the controller 1500A of the MPCC1500. As will be described further below, the controller 1500A executesprediction logic, such as prediction logic 850, in accordance with thedynamic control model 870 and based on the input parameters andconstraints, to predict future states of the WFGD process and to directcontrol of the WFGD subsystem 130′ so as to optimize the WFGD process.The controller 1500A then executes control generator logic, such ascontrol generator logic 860, in accordance with the control directivesfrom the prediction logic, to generate and issue control signals tocontrol specific elements of the WFGD subsystem 130′.

As previously described with reference to FIG. 15B, the input parametersinclude measured process parameters 1525, non-process parameters 1550,WFGD process constraints 1555, and estimated parameters 1560 computed bythe MPCC estimator 1500B executing estimation logic, such as estimationlogic 840, in accordance with the dynamic estimation model 880.

In the preferred implementation shown in FIG. 19, the measured processparameters 1525 include the ambient conditions 1520, the measured powergeneration system (PGS) load 1509, the measured inlet SO₂ 189, theblower load value 1502, the measured slurry pH 183, the measured outletSO₂ 1505, the lab measured gypsum purity 1506, the slurry pump statevalues 1512 and the slurry pump load values 1515. The WFGD processconstraints 1555 include the outlet SO₂ permit requirement 1922, thegypsum purity requirement 1924, the dissolved slurry O₂ requirement 1926and the slurry pH requirement 1928. The non-process inputs 1550 includetuning factors 1902, the current SO₂ credit price 1904, the current unitpower cost 1906, the current organic acid cost 1908, the current gypsumsale price 1910 and the future operating plans 1950. The estimatedparameters 1560 computed by the estimator 1500B include the calculatedgypsum purity 1932, the calculated dissolved slurry O₂ 1934, and thecalculated slurry PH 1936.

Because of the inclusion of non-process parameter inputs, e.g. thecurrent unit power cost 1906, the MPCC 1500 can direct control of theWFGD subsystem 130′ not only based on the current state of the process,but also based on the state of matters outside of the process.

Determining Availability of Additional SO₂ Absorption Capacity

As previously discussed with reference to FIG. 17, the MPCC 1500 cancontrol the state and load of the pumps 133A-133C and thereby controlthe flow of slurry 148′ to the different levels of the absorber tower132. The MPCC 1500 may can also compute the current power consumption ofthe pumps 133A-133C based on the current pump line-up and the currentpump load values 1515A-1515C, and additionally the current operationalcost for the pumps based on the computed power consumption and thecurrent unit power cost 1906.

The MPCC 1500 is preferably configured to execute the prediction logic850, in accordance with dynamic control model 870 and based on thecurrent pump state values 1512A-1512C and current pump load values1515A-1515C, to determine the available additional capacity of pumps133A-133C. The MPCC 1500 then determines, based on the determined amountof available additional pump capacity, the additional amount of SO₂which can be removed by adjusting the operation of the pumps e.g.turning on a pump to change the pump line-up or increasing the power toa pump.

Determining the Additional Amount of SO₂ Available for Removal

As noted above, in addition to the measured inlet SO₂ composition 189detected by sensor 188, the load 1509 of the power generation system(PGS) 110 is preferably detected by load sensor 1508 and also input as ameasured parameter to the MPCC 1500. The PGS load 1509 may, for example,represent a measure of the BTUs of coal being consumed in or the amountof power being generated by the power generation system 110. However,the PGS load 1509 could also represent some other parameter of the powergeneration system 110 or the associated power generation process, aslong as such other parameter measurement reasonably corresponds to theinlet flue gas load, e.g. some parameter of the coal burning powergeneration system or process which reasonably corresponds to thequantity of inlet flue gas going to the WFGD subsystem 130′.

The MPCC 1500 is preferably configured to execute the prediction logic850, in accordance with dynamic control model 870, to determine theinlet flue gas load, i.e. the volume or mass of the inlet flue gas 114,at the absorber tower 132, that corresponds to the PGS load 1509. TheMPCC 1500 may, for example, compute the inlet flue gas load at theabsorber tower 132 based on the PGS load 1509. Alternatively, a PGS load1509 could itself serve as the inlet flue gas load, in which case nocomputation is necessary. In either event, the MPCC 1500 will thendetermine the additional amount of SO₂ that is available for removalfrom the flue gas 114 based on the measured inlet SO₂ composition 189,the inlet flue gas load, and the measured outlet SO₂ 1505.

It should be recognized that the inlet flue gas load could be directlymeasured and input to the MPCC 1500, if so desired. That is, an actualmeasure of the volume or mass of the inlet flue gas 114 being directedto the absorber tower 132 could, optionally, be sensed by sensor (notshown) located upstream of the absorber tower 132 and downstream of theother APC subsystems 122 and fed to the MPCC 1500. In such a case, theremight be no need for the MPCC 1500 to determine the inlet flue gas loadthat corresponds to the PGS load 1509.

Instantaneous and Rolling Average SO₂ Removal Constraints

As described, with reference to FIG. 12, a process historian database1210 includes an SO₂ emission history database 890 as, for example,described with reference to FIG. 8. The process historian database 1210interconnects to the MPCC 1500. It should be understood that MPCC 1500could be of the type shown, for example, in FIG. 8, or could be amulti-tier type controller, such as a two tier controller as shown inFIG. 10.

The SO₂ emission history database 890 stores data representing the SO₂emissions, not just in terms of the composition of the SO₂ but also thepounds of SO₂ emitted, over the last rolling average period.Accordingly, in addition to having access to information representingthe current SO₂ emissions via the input measured outlet SO₂ 1505 fromthe SO₂ analyzer 1504, by interconnecting to the process historiandatabase 1210 the MPCC 1500 also has access to historical informationrepresenting the SO₂ emissions, i.e. the measured outlet SO₂, over thelast rolling-average time window via the SO₂ emissions history database890. It will be recognized that, while the current SO₂ emissionscorrespond to a single value, the SO₂ emissions over the lastrolling-average time window correspond to a dynamic movement of the SO₂emissions over the applicable time period.

Determining the Availability of Additional SO₂ Oxidation Capacity

As shown in FIG. 19 and discussed above, input to the MPCC 1500 aremeasured values of (1) the outlet SO₂ 1505, (2) the measured blower load1502, which corresponds to the amount of oxidation air entering thecrystallizer 134, (3) the slurry pump state values 1512, i.e. the pumplineup, and the slurry pump load values 1515, which correspond to theamount of the limestone slurry flowing to the absorber tower 132, (4)the measured pH 183 of the slurry flowing to the absorber tower 132.Additionally input to the MPCC 1500 are limit requirements on (1) thepurity 1924 of the gypsum byproduct 160′, (2) the dissolved O₂ 1926 inthe slurry within the crystallizer 134, which corresponds to the amountof dissolved O₂ in the slurry necessary to ensure sufficient oxidationand avoid blinding of the limestone, and (3) the outlet SO₂ 1922 in theflue gas 116′ exiting the WFGD subsystem 130′. Today, the outlet SO₂permit requirement 1922 will typically include constraints for both theinstantaneous SO₂ emissions and the rolling average SO₂ emissions. Alsoinput to MPCC 1500 are non-process inputs, including (1) the unit powercost 1906, e.g. the cost of a unit of electricity, and (2) the currentand/or anticipated value of an SO₂ credit price 1904, which representsthe price at which such a regulatory credit can be sold. Furthermore,the MPCC 1500 computes an estimate of (1) the current purity 1932 of thegypsum byproduct 160′, (2) the dissolved O₂ 1934 in the slurry withinthe crystallizer 134, and (3) the PH 1936 of the slurry flowing to theabsorber tower 132.

The MPCC 1500, executing the prediction logic in accordance with thedynamic control logic, processes these parameters to determine theamount of SO₂ being reacted on by the slurry in the absorber tower 132.Based on this determination, the MPCC 1500 can next determine the amountof non-dissolved O₂ that remains available in the slurry within thecrystallizer 134 for oxidation of the calcium sulfite to form calciumsulfate.

Determining Whether to Apply Additional Available Capacity

If the MPCC 1500 has determined that additional capacity is available toabsorb and oxidize additional SO₂ and there is additional SO₂ availablefor removal, the MPCC 1500 is also preferably configured to execute theprediction logic 850, in accordance with the dynamic control model 870,to determine whether or not to control the WFGD subsystem 130′ to adjustoperations to remove additional available SO₂ from the flue gas 114. Tomake this determination, the MPCC 1500 may, for example, determine ifthe generation and sale of such SO₂ credits will increase theprofitability of the WFGD subsystem 130′ operations, because it is moreprofitable to modify operations to remove additional SO₂, beyond thatrequired by the operational permit granted by the applicablegovernmental regulatory entity i.e. beyond that required by the outletSO₂ permit requirement 1922, and to sell the resulting regulatorycredits which will be earned.

In particular, the MPCC 1500, executing the prediction logic 850, inaccordance with the dynamic control model 870, will determine thenecessary changes in the operations of the WFGD subsystem 130′ toincrease the removal of SO₂. Based on this determination, the MPCC 1500will also determine the number of resulting additional regulatorycredits that will be earned. Based on the determined operational changesand the current or anticipated cost of electricity, e.g. unit power cost1906, the MPCC 1500 will additionally determine the resulting additionalelectricity costs required by the changes in the WFGD subsystem 130′operations determined to be necessary. Based on these laterdeterminations and the current or anticipated price of such credits,e.g. SO₂ credit price 1904, the MPCC 1500 will further determine if thecost of generating the additional regulatory credits is greater than theprice at which such a credit can be sold.

If, for example, the credit price is low, the generation and sale ofadditional credits may not be advantageous. Rather, the removal of SO₂at the minimal level necessary to meet the operational permit granted bythe applicable governmental regulatory entity will minimize the cost andthereby maximize the profitability of the WFGD subsystem 130′operations, because it is more profitable to remove only that amount ofSO₂ required to minimally meet the outlet SO₂ permit requirement 1922 ofthe operational permit granted by the applicable governmental regulatoryentity. If credits are already being generated under the WFGD subsystem130′ current operations, the MPCC 1500 might even direct changes in theoperation of the WFGD subsystem 130′ to decrease the removal of SO₂ andthus stop any further generation of SO₂ credits, and thereby reduceelectricity costs, and hence profitability of the operation.

Establishing Operational Priorities

As also shown in FIG. 19, MPCC 1500 is also preferably configured toreceive tuning factors 1902 as another of the non-process input 1550.The MPCC 1500, executing the prediction logic 850 in accordance with thedynamic control model 870 and the tuning factors 1902, can setpriorities on the control variables using, for example, respectiveweightings for each of the control variables.

In this regard, preferably the constraints 1555 will, as appropriate,establish a required range for each constrained parameter limitation.Thus, for example, the outlet SO₂ permit requirement 1922, the gypsumpurity requirement 1924, the dissolved O₂ requirement 1926 and theslurry pH requirement 1928 will each have high and low limits, and theMPCC 1500 will maintain operations of the WFGD subsystem 130′ within therange based on the tuning factors 1902.

Assessing the Future WFGD Process

The MPCC 1500, executing the prediction logic 850 in accordance with thedynamic process model 870, preferably first assesses the current stateof the process operations, as has been discussed above. However, theassessment need not stop there. The MPCC 1500 is also preferablyconfigured to execute the prediction logic 850, in accordance with thedynamic process model 870, to assess where the process operations willmove to if no changes in the WFGD subsystem 130′ operations are made.

More particularly, the MPCC 1500 assesses the future state of processoperations based on the relationships within the dynamic control model870 and the historical process data stored in the process historiandatabase 1210. The historical process data includes the data in the SO₂history database as well as other data representing what has previouslyoccurred within the WFGD process over some predefined time period. Aspart of this assessment, the MPCC 1500 determines the current path onwhich the WFGD subsystem 130′ is operating, and thus the future value ofthe various parameters associate with the WFGD process if no changes aremade to the operations.

As will be understood by those skilled in the art, the MPCC 1500preferably determines, in a manner similar to that discussed above, theavailability of additional SO₂ absorption capacity, the additionalamount of SO₂ available for removal, the availability of additional SO₂oxidation capacity and whether to apply additional available capacitybased on the determined future parameter values.

Implementing an Operating Strategy for WFGD Subsystem Operations

MPCC 1500 can be used as a platform to implement multiple operatingstrategies without impacting the underlying process model and processcontrol relationships in the process model. MPCC 1500 uses an objectivefunction to determine the operating targets. The objective functionincludes information about the process in terms of the relationships inthe process model, however, it also includes tuning factors, or weights.The process relationships represented in the objective function via theprocess model are fixed. The tuning factors can be adjusted before eachexecution of the controller. Subject to process limits or constraints,the controller algorithm can maximize or minimize the value of theobjective function to determine the optimum value of the objectivefunction. Optimal operating targets for the process values are availableto the controller from the optimum solution to the objective function.Adjusting the tuning factors, or weights, in the objective functionchanges the objective function value and, hence the optimum solution. Itis possible to implement different operating strategies using MPCC 1500by applying the appropriate criteria or strategy to set the objectivefunction tuning constants. Some of the more common operating strategiesmight include:

-   -   Asset optimization (maximize profit/minimize cost),    -   Maximize pollutant removal,    -   Minimize movement of the manipulated variables in the control        problem        Optimizing WFGD Subsystem Operations

Based on the desired operating criteria and appropriately tunedobjective function and the tuning factors 1902, the MPCC 1500 willexecute the prediction logic 850, in accordance with the dynamic processmodel 870 and based on the appropriate input or computed parameters, tofirst establish long term operating targets for the WFGD subsystem 130′.The MPCC 1500 will then map an optimum course, such as optimumtrajectories and paths, from the current state of the process variables,for both manipulated and controlled variables, to the respectiveestablish long term operating targets for these process variables. TheMPCC 1500 next generates control directives to modify the WFGD subsystem130′ operations in accordance with the established long term operatingtargets and the optimum course mapping. Finally, the MPCC 1500,executing the control generator logic 860, generates and communicatescontrol signals to the WFGD subsystem 130′ based on the controldirectives.

Thus, the MPCC 1500, in accordance with the dynamic control model 870and current measured and computed parameter data, performs a firstoptimization of the WFGD subsystem 130′ operations based on a selectedobjective function, such as one chosen on the basis of the currentelectrical costs or regulatory credit price, to determine a desiredtarget steady state. The MPCC 1500, in accordance with the dynamiccontrol model 870 and process historical data, then performs a secondoptimization of the WFGD subsystem 130′ operations, to determine adynamic path along which to move the process variables from the currentstate to the desired target steady state. Beneficially, the predictionlogic being executed by the MPCC 1500 determines a path that willfacilitate control of the WFGD subsystem 130′ operations by the MPCC1500 so as to move the process variables as quickly as practical to thedesired target state of each process variable while minimizing the erroror the offset between the desired target state of each process variableand the actual current state of each process variable at every pointalong the dynamic path.

Hence, the MPCC 1500 solves the control problem not only for the currentinstant of time (T0), but at all other instants of time during theperiod in which the process variables are moving from the current stateat T0 to the target steady state at Tss. This allows movement of theprocess variables to be optimized throughout the traversing of theentire path from the current state to the target steady state. This inturn provides additional stability when compared to movements of processparameters using conventional WFGD controllers, such as the PIDdescribed previously in the Background.

Optimized control of the WFGD subsystem is possible because the processrelationships are embodied in the dynamic control model 870, and becausechanging the objective function or the non-process inputs, such as theeconomic inputs or the tuning of the variables, does not impact theserelationships. Therefore, it is possible to manipulate or change the waythe MPCC 1500 controls the WFGD subsystem 130′, and hence the WFGDprocess, under different conditions, including different non-processconditions, without further consideration of the process level, once thedynamic control model has been validated.

Referring again to FIGS. 15A and 19, examples of the control of the WFGDsubsystem 130′ will be described for the objective function ofmaximizing SO₂ credits and for the objective function of maximizingprofitability or minimizing loss of the WFGD subsystem operations. Itwill be understood by those skilled in the art that by creating tuningfactors for other operating scenarios it is possible to optimize,maximize, or minimize other controllable parameters in the WFGDsubsystem.

Maximizing SO₂ Credits

To maximize SO₂ credits, the MPCC 1500, executes the prediction logic850, in accordance with the dynamic control model 870 having theobjective function with the tuning constants configured to maximize SO₂credits. It will be recognized that from a WFGD process point of view,maximizing of SO₂ credits requires that the recovery of SO₂ bemaximized.

The tuning constants that are entered in the objective function willallow the object function to balance the effects of changes in themanipulated variables with respect to SO₂ emissions relative to eachother.

The net result of the optimization will be that the MPCC 1500 willincrease:

-   -   SO₂ removal by increasing the slurry pH setpoint 186′, and    -   Increase blower oxidation air 154′ to compensate for the        additional SO₂ that is being recovered    -   Subject to constraints on:    -   The low limit on the gypsum purity constraint 1924. It will be        recognized that this will typically be a value providing a        slight margin of safety above the lowest allowable limit of        gypsum purity within the gypsum purity requirement 1924.    -   The low limit on required oxidation air 154′, and    -   The maximum capacity of the oxidation air blower 150.

In addition, If MPCC 1500 is allowed to adjust the pump 133 line-up,MPCC 1500 will maximize slurry circulation and the effective slurryheight subject to constraints on pump 133 line-up and loading.

Under this operating scenario, MPCC 1500 is focused totally onincreasing SO₂ removal to generate SO₂ credits. MPCC 1500 will honorprocess constraints such as gypsum purity 1924 and oxidation airrequirements. But, this scenario does not provide for a balance betweenthe cost/value of electrical power vs. the value of SO₂ credits. Thisscenario would be appropriate when the value of SO₂ credits far exceedsthe cost/value of electrical power.

Maximizing Profitability or Minimizing Losses

The objective function in MPCC 1500 can be configured so that it willmaximize profitability or minimize losses. This operating scenario couldbe called the “asset optimization” scenario. This scenario also requiresaccurate and up-to-date cost/value information for electrical power, SO₂credits, limestone, gypsum, and any additives such as organic acid.

Cost/value factors associated with each of the variables in thecontroller model are entered into the objective function. Then, theobjective function in MPCC 1500 is directed to minimize cost/maximizeprofit. If profit is defined as a negative cost, then cost/profitbecomes a continuous function for the objective function to minimize.

Under this scenario, the objective function will identify minimum costoperation at the point where the marginal value of generating anadditional SO₂ credit is equal to the marginal cost of creating thatcredit. It should be noted that the objective function is a constrainedoptimization, so the minimize cost solution will be subject toconstraints on:

-   -   Minimum SO₂ removal (for compliance with emission        permits/targets),    -   Minimum gypsum purity,    -   Minimum oxidation air requirement,    -   Maximum blower load,    -   Pump line-up and loading limits,    -   Additive limits.

This operating scenario will be sensitive to changes in both thevalue/cost of electricity and the value/cost of SO₂ credits. For maximumbenefit, these cost factors should be updated in real-time.

For example, assuming that the cost factors are updated before eachcontroller 1500A execution, as electricity demand increases during theday, the spot value of the electrical power being generated alsoincreases. Assuming that it is possible for the utility to selladditional power at this spot value and value of SO₂ credits areessentially fixed at the current moment, then if there is a way to shiftpower from the pumps 133 and the blower 150 to the grid while stillmaintaining the minimum SO₂ removal, there is significant economicincentive to put the additional power on the grid. The cost/value factorassociated with electrical power in the MPCC 1500 objective functionwill change as the spot value of electricity changes and the objectivefunction will reach a new solution that meets the operating constraintsbut uses less electrical power.

Conversely, if the spot value of an SO₂ credit increases, there is amarket for additional SO₂ credits, and the cost/value of electricalpower is relatively constant, the objective function in MPCC 1500 willrespond to this change by increasing SO₂ removal subject to theoperating constraints.

In both example scenarios, MPCC 1500 will observe all operatingconstraints, and then the objective function in MPCC 1500 will seek theoptimum operating point were the marginal value of an SO₂ credit isequal to the marginal cost required to generate the credit.

Infeasible Operation

It is possible that at times the WFGD Subsystem 130′ will presented witha set of constraints 1555 and operating conditions, measured 1525 andestimated 1560, for which there is no feasible solution; the area offeasible operation 525 as shown in FIGS. 5A and 5B is null space. Whenthis occurs, no solution will satisfy all of the constraints 1555 on thesystem. This situation can be defined as “infeasible operation” becauseit is infeasible to satisfy the constraints on the system.

Infeasible operation may be the result of operation beyond thecapability of the WFGD, a process upset in either the WFGD or upstreamof the WFGD. It may also be the result of overly restrictive,inappropriate, and/or incorrect constraints 1555 on the WFGD and theMPCC 1500 system.

During a period of infeasible operation, the objective function in MPCC1500 focuses on the objective to minimize weighted error. Each processconstraint 1555 appears in the objective function. A weighting term isapplied to each error or violation of the constraint limit by thecontrolled/targeted process value. During controller 1500Acommissioning, the implementation engineer(s) select appropriate valuesfor the error weighting terms so that during periods of infeasibleoperation the objective function will “give-up” on constraints with theleast weight in order to honor the more important constraints.

For example, in the WFGD subsystem 130′, there are regulatory permitlimits associated with the outlet SO₂ 1505 and a sales specificationassociated with gypsum purity 1506. Violation of the SO₂ emission permitcarries fines and other significant ramifications. Violation of thegypsum purity sales specification requires downgrading or re-mixing ofthe gypsum product. Downgrading product is not a desirable option, butit has less impact on the operating viability of the generation stationthan violation of the emission permit. Hence, the tuning factors will beset so that the constraint on the SO₂ emission limit will have moreimportance, a greater weight, than the constraint on gypsum purity. Sowith these tuning factors, during periods of infeasible operation, theobjective function in MPCC 1500 will preferentially maintain SO₂emissions at or under the SO₂ emission limit and violate the gypsumpurity constraint; MPCC 1500 will minimize violation of the gypsumpurity constraint, but it will shift the infeasibility to this variableto maintain the more important emission limit.

Notifying Operators of Control Decisions

The MPCC 1500 is also preferably configured to provide notices tooperators of certain MPCC 1500 determinations. Here also, the predictionlogic 850, dynamic control model 870 or other programming may be used toconfigure the MPCC 1500 to provide such notices. For example, the MPCCmay function to direct the sounding of alarms or presentation of text orimage displays, so that operators or other users are aware of certaindeterminations of the MPCC 1500, such as a determination thatmaintaining gypsum quality is of low priority at a particular timebecause SO₂ credits are so valuable.

WFGD Summary

In summary, as described in detail above, the optimization-based controlfor a WFGD process has been described. This control facilitates themanipulation of the setpoints for the WFGD process in real-time basedupon the optimization of a multiple-input, multiple-output model whichis updated using process feedback. The optimization can take multipleobjectives and constraints for the process into account. Without suchcontrol, the operator must determine the setpoints for the WFGD. Becauseof the complexity of the process, the operator often chooses suboptimalsetpoints for balancing multiple constraints and objectives. Suboptimalsetpoints/operation results in lost removal efficiency, higher operatingcosts and potential violations of quality constraints.

Also described is a virtual on-line analysis for gypsum purity. Theanalysis computes an on-line estimate of the purity of the gypsumbyproduct being produced by the WFGD process using measured processvariables, lab analysis and a dynamic estimation model for gypsumpurity. Since on-line sensors for gypsum purity produced by WFGDprocessing are not conventionally available, off-line lab analysis areconventionally used to determine gypsum purity. However, because gypsumpurity is only occasionally tested, and the purity must be maintainedabove a constraint, typically set in the gypsum specification, processoperators often use setpoints for the WFGD process that result in thegypsum purity being well above the required constraint. This in turnresults in SO₂ removal efficiency being sacrificed and/or unnecessarypower consumption by the WFGD subsystem. By estimating gypsum purityon-line, setpoints for the WFGD process can be controlled to ensure thegypsum purity closer to the purity constraint, thus, facilitatingincreased SO₂ removal efficiency.

As also described in detail above, the virtual on-line analysis ofgypsum purity is preformed in a control loop, thus allowing estimates tobe included in the feedback control, whether the model predictivecontrol (MPC) or PID control is utilized. By providing feedback to acontrol loop, the SO₂ removal efficiency can be increased when operatingso as to produce gypsum with purity closer to the applicable purityconstraint.

Additionally described above is a virtual on-line analysis foroperational costs. The analysis, as disclosed, uses WFGD process data aswell as current market pricing data to compute the operation costs of aWFGD process on-line. Conventionally, operators do not account for thecurrent cost of operating a WFGD process. However, by computing suchcost on-line, operators are now given the ability to track the effectsof process changes, e.g. changes in the setpoints, on operational cost.

Further described above is the performance of the virtual on-lineanalysis of operational cost in the control loop, thus allowingestimates to be included in the feedback control, irrespective ofwhether MPC or PID is utilized. This feedback control can thereby beexercised to minimize the operational costs.

Also described above is a technique for applying MPC control to optimizeoperation of the WFGD process for maximum SO₂ removal efficiency,minimum operational costs and/or the desired gypsum purity above aconstraint. Such control may take advantage of a virtual analysis ofgypsum purity and/or operational cost within the feedback loop, asdiscussed above, and is capable of automatic optimization, for exampleof the SO₂ removal efficiency and/or the operational costs for a WFGDprocess.

Necessary as well as optional parameters are described. With thedisclosed parameters those skilled in the art can apply well knowntechniques in a routine manner to develop an appropriate model of theapplicable WFGD process, which can in turn be utilized, for example by aMPCC 1550 controlling the WFGD process, to optimize operation of theWFGD process. Models may be developed for gypsum purity, SO₂ removalefficiency and/or operational costs, as well as various other factors.Conventional MPC or other logic can be executed based on the WFGDprocess models developed in accordance with the principles, systems andprocesses described herein, to optimize the WFGD process. Thus, thelimitations of conventional control of WFGD processes, for example usingPIDs, which are limited to single-input/single-output structures andstrictly rely on process feedback, rather than process models, areovercome. By including models in the feedback loop, the WFGD processcontrol can be even further enhanced to, for example, maintainoperations closer to constraints with lower variability than ever beforepossible.

The application of neural network based models for a WFGD process isalso described in both the context of process control and virtualon-line analysis of a WFGD process. As described in detail above, theinput to output relationships of a WFGD process exhibits a nonlinearrelationship, therefore making it advantageous to use a nonlinear model,since such a model will best represent the nonlinearity of the process.Furthermore, the development of other models derived using empiricaldata from the WFGD process is also described.

The application of a combination model, which considers both firstprinciples and empirical process data, for control and virtual analysisof a WFGD process is also described in detail above. While some elementsof the WFGD process are well understood and may be modeled using firstprinciple models, other elements are not so well understood and aretherefore most conveniently modeled using historical empirical processdata. By using a combination of first principles and empirical processdata, an accurate model can be developed quickly without the need tostep test all elements of the process.

A technique for validating sensor measurements used in a WFGD process isalso described above in detail. As described, non-validated measurementscan be replaced, thereby avoiding improper control resulting frominaccurate sensor measurements of the WFGD process. By validating andreplacing bad measurements, the WFGD process can now be continuousoperated based upon the correct process values.

The control of rolling emissions is also described in detail. Thus, inview of the present disclosure, the WFGD process can be controlled sothat one or more multiple rolling emissions average for the process canbe properly maintained. The MPC can be implemented using a singlecontroller or multiple cascaded controllers to control the process.Using the described technique, the WFGD process can be controlled, forexample, such that multiple rolling averages are simultaneous consideredand maintained while at the same time operational costs are minimized.

SCR Subsystem Architecture:

Highlights from the application of MPCC to the SCR will be described todemonstrate the usefulness of the present invention to otherenvironments and implementations. The main control objectives for theSCR involve:

-   -   NOx removal—targeted for either regulatory compliance or asset        optimization,    -   Control of ammonia slip, and    -   Minimum cost operation—management of SCR catalyst and ammonia        usage.

Once again, a measurement and control methodology similar to what wasdiscussed with the WFGD can be utilized:

Measurement: As discussed, ammonia slip is an important controlparameter that is frequently not measured. If there is not a directmeasurement of ammonia slip, it is possible to calculate ammonia slipfrom the inlet and outlet NOx measurements 2112 and 2111 and the ammoniaflow to the SCR 2012. The accuracy of this calculation is suspectbecause it requires accurate and repeatable measurements and involvesevaluating small differences between large numbers. Without a directmeasurement of ammonia slip, virtual on-line analyzer techniques areused in addition to a direct calculation of ammonia slip to create ahigher fidelity ammonia slip estimate.

The first step in the VOA estimates the catalyst potential (reactioncoefficient) and the space velocity correlation variance (SVCV) acrossthe SCR catalyst. These are computed using inlet flue gas flow,temperature, total operational time of the catalyst, and quantities ofinlet NO_(x) and outlet NO_(x). Both the calculation of catalystpotential and SVCV are time averaged over a number of samples. Thecatalyst potential changes slowly, thus, many data points are used tocompute the potential while the SVCV changes more often so relativelyfew data points are used to compute the SVCV. Given the catalystpotential (reaction coefficient), the space velocity correlationvariance (SVCV), and the inlet NO_(x), an estimate of ammonia slip maybe computed using the technique shown in FIG. 9.

If an ammonia slip hardware sensor is available, a feedback loop fromsuch a sensor to the process model will be used to automatically biasthe VOA. The VOA would be used to significantly reduce the typicallynoisy output signal of the hardware sensor.

Finally, it should be noted that virtual on-line analyzer foroperational cost of the SCR can be used. As outlined in the previoussection, the model for operation costs is developed from firstprinciples. The operational costs can be computed on-line using avirtual on-line analyzer—again, the technique that is shown in FIG. 9 isused for the VOA.

Control: MPCC is applied to the SCR control problem to achieve thecontrol objectives. FIG. 22, similar to FIG. 8 shows the MPCC structurefor the SCR MPCC 2500. Because of the similarities to FIG. 8, a detaileddiscussion of FIG. 22 is not necessary, as MPCC 2500 will be understoodfrom the discussion of FIG. 8 above. FIG. 23A shows the application ofMPCC 2500 to the SCR Subsystem 2170′. The biggest change to the SCRSubsystem 2170′ regulatory control scheme is that functionality of theNOx Removal PID controller 2020 and the load feedforward controller2220, each shown in FIG. 20, are replaced with MPCC 2500. MPCC 2500directly calculates the ammonia flow SP 2021A′ for use by the ammoniaflow controller(s) (PID 2010).

MPCC 2500 can adjust one or a plurality to ammonia flows to control NOxremoval efficiency and ammonia slip. Provided that there are sufficientmeasurement values with the inlet and outlet NOx analyzers 2003 and 2004and the ammonia slip measurement 2611 from ammonia analyzer 2610 toestablish NOx removal efficiency and ammonia profile information, MPCC2500 will control the overall or average NOx removal efficiency andammonia slip and also the profile values. Coordinated control of aplurality of values in the NOx removal efficiency and ammonia slipprofile allows for a significant reduction in variability around theaverage process values. Lower variability translates into fewer “hot”stops within the system. This profile control requires at least someform of profile measure and control—more than one NOx inlet, NOx outletand ammonia slip measurement and more than one dynamically adjustableammonia flow. It must be acknowledged that without the necessary inputs(measurements) and control handles (ammonia flows), the MPCC 2500 willnot be able to implement profile control and capture the resultingbenefits.

From the perspective of MPCC 2500, the additional parameters associatedwith profile control increase the size of the controller, but theoverall control methodology, scheme, and objectives are unchanged.Hence, future discussion will consider control of the SCR subsystemwithout profile control.

FIG. 23B shows an overview of MPCC 2500.

Optimizing SCR Subsystem Operations

Based on the desired operating criteria and appropriately tunedobjective function and the tuning factors 2902, the MPCC 2500 willexecute the prediction logic 2850, in accordance with the dynamiccontrol model 2870 and based on the appropriate input or computedparameters, to first establish long term operating targets for the SCRsubsystem 2170′. The MPCC 2500 will then map an optimum course, such asoptimum trajectories and paths, from the current state of the processvariables, for both manipulated and controlled variables, to therespective establish long term operating targets for these processvariables. The MPCC 2500 next generates control directives to modify theSCR subsystem 2170′ operations in accordance with the established longterm operating targets and the optimum course mapping. Finally, the MPCC2500, executing the control generator logic 2860, generates andcommunicates control signals to the SCR subsystem 2170′ based on thecontrol directives.

Thus, the MPCC 2500, in accordance with the dynamic control model andcurrent measure and computed parameter data, performs a firstoptimization of the SCR subsystem 2170′ operations based on a selectedobjective function, such as one chosen on the basis of the currentelectrical costs or regulatory credit price, to determine a desiredtarget steady state. The MPCC 2500, in accordance with the dynamiccontrol model and process historical data, then performs a secondoptimization of the SCR subsystem 2170′ operations, to determine adynamic path along which to move the process variables from the currentstate to the desired target steady state. Beneficially, the predictionlogic being executed by the MPCC 2500 determines a path that willfacilitate control of the SCR subsystem 2170′ operations by the MPCC2500 so as to move the process variables as quickly as practicable tothe desired target state of each process variable while minimizing theerror or the offset between the desired target state of each processvariable and the actual current state of each process variable at everypoint along the dynamic path.

Hence, the MPCC 2500 solves the control problem not only for the currentinstant of time (T0), but at all other instants of time during theperiod in which the process variables are moving from the current stateat T0 to the target steady state at Tss. This allows movement of theprocess variables to be optimized throughout the traversing of entirepath from the current state to the target steady state. This in turnprovides additional stability when compared to movements of processparameters using conventional SCR controllers, such as the PID describedpreviously.

The optimized control of the SCR subsystem is possibly because theprocess relationships are embodied in the dynamic control model 2870,and because changing the objective function or the non-process inputssuch as the economic inputs or the tuning of the variables, does notimpact these relationships. Therefore, it is possible to manipulate orchange the way the MPCC 2500 controls the SCR subsystem 2170′, and hencethe SCR process, under different conditions, including differentnon-process conditions, without further consideration of the processlevel, once the dynamic control model has been validated.

Referring again to FIGS. 23A and 23B, examples of the control of the SCRsubsystem 2170′ will be described for the objective function ofmaximizing NOx credits and for the objective function of maximizingprofitability or minimizing loss of the SCR subsystem operations. Itwill be understood by those skilled in the art that by creating tuningfactors for other operating scenarios it is possible to optimize,maximize, or minimize other controllable parameters in the SCRsubsystem.

Maximizing NOx Credits

To maximize NOx credits, the MPCC 2500, executes the prediction logic2850, in accordance with the dynamic control model 2870 having theobjective function with the tuning constants configured to maximize NOxcredits. It will be recognized that from a SCR process point of view,maximizing of NOx credits requires that the recovery of NOx bemaximized.

The tuning constants that are entered into the objective function willallow the objective function to balance the effect of changes in themanipulated variables with respective to NOx emissions.

The net results of the optimization will be that the MPCC 2500 willincrease:

-   -   NOx removal by increasing the ammonia flow setpoint(s) subject        to constraints on:    -   Maximum ammonia slip.

Under this operating scenario, MPCC 2500 is focused totally onincreasing NOx removal to generate NOx credits. MPCC 2500 will honor theprocess constraint on ammonia slip. But, this scenario does not providefor a balance between the cost/value of ammonia or ammonia slip vs. thevalue of the NOx credits. This scenario would be appropriate when thevalue of NOx credits far exceeds the cost/value of ammonia and ammoniaslip.

Maximizing Profitability or Minimizing Losses

The objective function in MPCC 2500 can be configured so that it willmaximize profitability or minimize losses. This operating scenario couldbe called the “asset optimization” scenario. This scenario also requiresaccurate and up-to-date cost/value information for electrical power, NOxcredits, ammonia, and the impact of ammonia slip on downstreamequipment.

Cost/value factors associated with each of the variables in thecontroller model are entered into the objective function. Then, theobjective function in MPCC 2500 is directed to minimize cost/maximizeprofit. If profit is defined as a negative cost, then cost/profitbecomes a continuous function for the objective function to minimize.

Under this scenario, the objective function will identify minimum costoperation at the point where the marginal value of generating anadditional NOx credit is equal to the marginal cost of creating thatcredit. It should be noted that the objective function is a constrainedoptimization, so the minimize cost solution will be subject toconstraints on:

-   -   Minimum NOx removal (for compliance with emission        permits/targets),    -   Minimum ammonia slip,    -   Minimize ammonia usage

This operating scenario will be sensitive to changes in both thevalue/cost of electricity and the value/cost of NOx credits. For maximumbenefit, these cost factors should be updated in real-time.

For example, assuming that the cost factors are updated before eachcontroller execution, as electricity demand increases during the day,the spot value of the electrical power being generated also increases.Assuming that it is possible for the utility to sell additional power atthis spot value and value of NOx credits are essentially fixed at thecurrent moment, then there is significant incentive to minimize ammoniaslip because this will keep the air preheater cleaner and allow moreefficient generation of power. There is a significant economic incentiveto put the additional power on the grid. The cost/value factorassociated with electrical power in the MPCC 2500 objective functionwill change as the spot value of electricity changes and the objectivefunction will reach a new solution that meets the operating constraintsbut uses less electrical power.

Conversely, if the spot value of a NOx credit increases, there is amarket for additional NOx credits, and the cost/value of electricalpower is relatively constant, the objective function in MPCC 2500 willrespond to this change by increasing NOx removal subject to theoperating constraints.

In both example scenarios, MPCC 2500 will observe all operatingconstraints, and then the objective function in MPCC 2500 will seek theoptimum operating point were the marginal value of a NOx credit is equalto the marginal cost required to generate the credit.

SUMMARY

It will also be recognized by those skilled in the art that, while theinvention has been described above in terms of one or more preferredembodiments, it is not limited thereto. Various features and aspects ofthe above described invention may be used individually or jointly.Further, although the invention has been described in detail of thecontext of its implementation in a particular environment and forparticular purposes, e.g. wet flue gas desulfurization (WFGD) with abrief overview of selective catalytic reduction (SCR), those skilled inthe art will recognize that its usefulness is not limited thereto andthat the present invention can be beneficially utilized in any number ofenvironments and implementations. Accordingly, the claims set forthbelow should be construed in view of the full breath and spirit of theinvention as disclosed herein.

1. A parameter value estimator for a process primarily performed tocontrol emission of a particular non-particulate pollutant into the air,the process having multiple process parameters (MPPs) including aparameter representing an amount of the particular non-particulatepollutant emitted, comprising: one of a neural network process model anda non-neural network process model representing a relationship betweenone of the MPPs, other than the parameter representing the amount of theemitted particular non-particulate pollutant, and one or more of theother MPPs; and a processor configured with the logic to estimate avalue of the one MPP based on a value of each of the one or more otherMPPs and the one model; wherein the one MPP is either (i) a pH level ofmatter applied in the process to absorb the particular nonparticulatepollutant and thereby control its emission into the air, (ii) a purityof a by-product produced in performing the process, (iii) an amount ofoxygen dissolved in matter applied in the process to absorb theparticular non-particulate pollutant and thereby control its emissioninto the air, (iv) an amount of ammonia applied in the process to absorbthe particular non-particulate pollutant that is emitted into the airwith the particular non-particulate pollutant that is not absorbed, or(v) an amount of applied ammonia in exhausted reduced NO_(x) flue gas,the particular non-particulate pollutant being NO_(x), and the one ormore other MPPs include an amount of the applied ammonia.
 2. Theparameter value estimator according to claim 1, wherein: the particularnon-particulate pollutant is one of NO_(x) and SO₂.
 3. The parametervalue estimator according to claim 1, wherein: the value of each of theone or more other MPPs is either (i) a value measured during theperformance of the process or (ii) a value estimated based on one ormore other values of the multiple MPPs measured during the performanceof the process.
 4. The parameter value estimator according to claim 1,wherein: the estimated value of the one MPP is an estimated first valueof the one MPP; the value of each of the one or more other MPPs is afirst value of each of the one or more other MPPs; and the processor isfurther configured with the logic to estimate a second value of the oneMPP based on the estimated first value of the one MPP, a second value ofeach of the one or more other MPPs and the one model.
 5. The parametervalue estimator according to claim 1, wherein: the processor is furtherconfigured to at least one of (i) estimate the value of the one MPP inreal time during performance of process and (ii) estimate the value ofthe one MPP periodically.
 6. The parameter value estimator according toclaim 1, wherein: the processor is configured with logic includingestimate generator logic and estimator logic; the processor estimatesthe value of the one MPP by: executing the estimate generator logic tocompute a value of the one MPP based on the value of each of the one ormore other MPPs and the one model; and executing the estimator logic todetermine the estimated value of the one MPP based on the computed valueof the one MPP and a measured value of the one MPP; and the processor isfurther configured with the logic to update the one model based on thedetermined estimated value of the one MPP.
 7. The parameter valueestimator according to claim 6, wherein: the updating includes updatingthe represented relationship between the one MPP and the one or moreother MPPs based on the determined estimated value of the one MPP. 8.The parameter value estimator according to claim 6, wherein: theestimator logic includes a Kalman filter for filtering the computed andthe measured values of the one MPP to determine the estimated value ofthe one MPP.
 9. The parameter value estimator according to claim 1,wherein: the process is a selective catalytic reduction (SOR) processthat applies ammonia to remove NO_(x) from the NO_(x) laden flue gas andthereby control emissions of NO_(x), and exhausts reduced NO_(x) fluegas; and the one MPP is an amount of the applied ammonia in theexhausted reduced NO_(x) flue gas and the one or more other MPPsincludes an amount of the applied ammonia.
 10. The parameter valueestimator according to claim 1, wherein: the process is a wet flue gasdesulfurization (WFGD) process that distributes limestone slurry,applies the distributed limestone slurry to remove SO₂ from SO₂ ladenwet flue gas and thereby control emissions of SO₂, and exhaustsdesulfurized flue gas; the one MPP is a pH level of the appliedlimestone slurry; and the one or more other MPPs includes at least oneof an amount of SO₂ in the SO₂ laden wet flue gas, an amount of SO₂ inthe exhausted desulfurized flue gas, and the distribution of the appliedlimestone slurry.
 11. The parameter value estimator according to claim1, wherein: the process is a wet flue gas desulfurization (WFGD) processthat (i) applies oxidation air to limestone slurry, (ii) distributes theoxidized limestone slurry, (iii) applies the distributed limestoneslurry to remove and crystallize SO₂ from SO₂ laden wet flue gas andthereby control emissions of SO₂ and produce gypsum as a by-product, and(iv) exhausts desulfurized flue gas; the one MPP is either a quality ofthe produced gypsum or an amount of dissolved oxygen in the oxidizedlimestone slurry; and the one or more other MPPs includes at least one apH level of the applied limestone slurry, a distribution of the appliedlimestone slurry and an amount of the applied oxidation air.
 12. Anarticle of manufacture for estimating a parameter value for a processperformed primarily to control emission of a particular non-particulatepollutant into the air, the process having multiple process parameters(MPPs) including a parameter representing an amount of the particularnon-particulate pollutant emitted, comprising: computer readable storagemedia; and logic stored on the storage media, wherein the stored logicis configured to be readable by one or more computers and thereby causethe one or more computers to operate so as to: detemine a value of eachof one or more of the MPPs; and estimate a value of another one of theMPPs, other than the parameter representing the amount of the emittedparticular non-particulate pollutant, based on (i) the determined valueof each of the one or more MPPs and (ii) one of a neural network processmodel and a non-neural network process model representing a relationshipbetween the one other MPP and the one or more MPPs; wherein the oneother MPP is either (i) a pH level of matter applied in the process toabsorb the particular non-particulate pollutant and thereby control itsemission into the air, (ii) a purity of a by-product produced inperforming the process, (iii) an amount of oxygen dissolved in matterapplied in the process to absorb the particular non-particulatepollutant and thereby control its emission into the air, (iv) an amountof ammonia applied in the process to absorb the particularnon-particulate pollutant that is emitted into the air with theparticular non-particulate pollutant that is not absorbed, or (v) anamount of applied ammonia in exhausted reduced NO_(x) flue gas, theparticular non-particulate pollutant being NO_(x), and the one or moreMPPs include an amount of the applied ammonia.
 13. The article ofmanufacture according to claim 12, wherein: the value of each of the oneor more MPPs is determined by either (i) measuring the value during theperformance of the process or (ii) estimating the value based on one ormore other values of the multiple MPPs measured during the performanceof the process.
 14. The article of manufacture according to claim 12,wherein: the estimated value of the one other MPP is an estimated firstvalue of the one other MPP and the value of each of the one or more MPPsis a first value of each of the one or more MPPs; and the stored logicis also configured to cause the one or more computers to operate so asto estimate a second value of the one other MPP based on the estimatedfirst value of the one other MPP, a second value of each of the one ormore MPPs, and the one model.
 15. The article of manufacture accordingto claim 12, wherein: the value of the one other MPP is at least one of(i) estimated in real time during performance of process and (ii)estimated periodically.
 16. The article of manufacture according toclaim 12, wherein: the process is a selective catalytic reduction (SCR)process that applies ammonia to remove NO_(x) from the NO_(x) laden fluegas and thereby control emissions of NO_(x), and exhausts reduced NO_(x)flue gas; and the one other MPP is an amount of the applied ammonia inthe exhausted reduced NO_(x) flue gas and the one or more MPPs includean amount of the applied ammonia.
 17. The article of manufactureaccording to claim 12, wherein: the process is a wet flue gasdesulfurization (WFGD) process that distributes limestone slurry,applies the distributed limestone slurry to remove SO₂ from SO₂ ladenwet flue gas and thereby control emissions of SO₂, and exhaustsdesulfurized flue gas; the one other MPP is a pH level of the appliedlimestone slurry; and the one or more MPPs include at least one of anamount of SO₂ in the SO₂ laden wet flue gas, an amount of SO₂ in theexhausted desulfurized flue gas, and the distribution of the appliedlimestone slurry.
 18. The article of manufacture according to claim 12,wherein: the process is a wet flue gas desulfurization (WFGD) processthat (i) applies oxidation air to limestone slurry, (ii) distributes theoxidized limestone slurry, (iii) applies the distributed limestoneslurry to remove and crystallize SO₂ from SO₂ laden wet flue gas andthereby control emissions of SO₂ and produce gypsum as a by-product, and(iv) exhausts desulfurized flue gas; the one other MPP is either aquality of the produced gypsum or an amount of dissolved oxygen in theoxidized limestone slurry; and the one or more MPPs include at least oneof a pH level of the applied limestone slurry, the distribution of theapplied limestone slurry and an amount of the applied oxidation air. 19.The article of manufacture according to claim 12, wherein: estimatingthe value of the one other MPP includes computing a value of the oneother MPP based on the value of each of the one or more MPPs and the onemodel, and determining the estimated value of the one other MPP based onthe computed value of the one other MPP and a measured value of the oneother MPP; and the stored logic is also configured to cause the one ormore computers to operate so as to update the one model based on thedetermined estimated value of the one other MPP.
 20. The article ofmanufacture according to claim 19, wherein: the updating includesupdating the represented relationship between the one other MPP and theone or more MPPs based on the determined estimated value of the oneother MPP.
 21. The article of manufacture according to claim 19,wherein: the estimated value of the one other MPP is determined byfiltering the computed and the measured values of the one other MPP witha Kalman filter.